Soil Moisture and Weather Data from Southern Rocky Mountain

Author(s):  
Madeline Schreiber
2021 ◽  
Author(s):  
A.K. Gilmer ◽  
et al.

<div>Table S1: Whole-rock compositions of analyzed samples. Table S2: Major and trace element geochemistry of feldspar. Table S3: Major and trace element geochemistry of pyroxene. Table S4: Major and trace element geochemistry of biotite. Table S5: Major and trace element geochemistry of amphibole. Table S6: Zircon geochronology and trace element geochemistry. Table S7: Lutetium and hafnium isotopic compositions of zircon. Table S8: Amphibole-plagioclase thermometry. Table S9: Sample locations and lithologies.<br></div>


2021 ◽  
Vol 12 (4) ◽  
pp. 1371-1391
Author(s):  
Raed Hamed ◽  
Anne F. Van Loon ◽  
Jeroen Aerts ◽  
Dim Coumou

Abstract. The US agriculture system supplies more than one-third of globally traded soybean, and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and we explore the synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis and satellite-informed root zone soil moisture fields with subnational crop yields using statistical methods that account for interaction effects. On average, our models can explain about two-thirds of the year-to-year yield variability (70 % for all years and 60 % for out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions and is less problematic during wet conditions. Compounding and interacting hot and dry (hot–dry) summer conditions (defined by the 95th and 5th percentiles of temperature and soil moisture respectively) reduce yields by 2 standard deviations. This sensitivity is 4 and 3 times larger than the sensitivity to hot or dry conditions alone respectively. Other relevant drivers of negative yield responses are lower temperatures early and late in the season, excessive precipitation in the early season, and dry conditions in the late season. We note that the sensitivity to the identified drivers varies across the spatial domain. Higher latitudes, and thus colder regions, are positively affected by high temperatures during the summer period. On the other hand, warmer southeastern regions are positively affected by low temperatures during the late season. Historic trends in identified drivers indicate that US soybean production has generally benefited from recent shifts in weather except for increasing rainfall in the early season. Overall, warming conditions have reduced the risk of frost in the early and late seasons and have potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over the eastern US. Nevertheless, despite these positive changes, we show that the frequency of compound hot–dry summer events has remained unchanged over the 1946–2016 period. In the longer term, climate models project substantially warmer summers for the continental US, although uncertainty remains as to whether this will be accompanied by drier conditions. This highlights a critical element to explore in future studies focused on US agricultural production risk under climate change.


2019 ◽  
Vol 20 (11) ◽  
pp. 5225-5250
Author(s):  
F. Tomek ◽  
A .K. Gilmer ◽  
M. S. Petronis ◽  
P. W. Lipman ◽  
M. S. Foucher

2008 ◽  
Vol 9 (4) ◽  
pp. 660-676 ◽  
Author(s):  
Venkataramana Sridhar ◽  
Kenneth G. Hubbard ◽  
Jinsheng You ◽  
Eric D. Hunt

Abstract This paper examines the role of soil moisture in quantifying drought through the development of a drought index using observed and modeled soil moisture. In Nebraska, rainfall is received primarily during the crop-growing season and the supply of moisture from the Gulf of Mexico determines if the impending crop year is either normal or anomalous and any deficit of rain leads to a lack of soil moisture storage. Using observed soil moisture from the Automated Weather Data Network (AWDN), the actual available water content for plants is calculated as the difference between observed or modeled soil moisture and wilting point, which is subsequently normalized with the site-specific, soil property–based, idealistic available water for plants that is calculated as the difference between field capacity and wilting point to derive the soil moisture index (SMI). This index is categorized into five classes from no drought to extreme drought to quantitatively assess drought in both space and time. Additionally, with the aid of an in-house hydrology model, soil moisture was simulated in order to compute model-based SMI and to compare the drought duration and severity for various sites. The results suggest that the soil moisture influence, a positive feedback process reported in many earlier studies, is unquestionably a quantitative indicator of drought. Also, the severity and duration of drought across Nebraska has a clear gradient from west to east, with the Panhandle region experiencing severe to extreme drought in the deeper soil layers for longer periods (&gt;200 days), than the central and southwestern regions (125–150 days) or the eastern regions about 100 days or less. The anomalous rainfall years can eliminate the distinction among these regions with regard to their drought extent, severity, and persistence, thus making drought a more ubiquitous phenomenon, but the recovery from drought can be subject to similar gradations. The spatial SMI maps presented in this paper can be used with the Drought Monitor maps to assess the local drought conditions more effectively.


2015 ◽  
Vol 16 (3) ◽  
pp. 1397-1408 ◽  
Author(s):  
Hongshuo Wang ◽  
Jeffrey C. Rogers ◽  
Darla K. Munroe

Abstract Soil moisture shortages adversely affecting agriculture are significantly associated with meteorological drought. Because of limited soil moisture observations with which to monitor agricultural drought, characterizing soil moisture using drought indices is of great significance. The relationship between commonly used drought indices and soil moisture is examined here using Chinese surface weather data and calculated station-based drought indices. Outside of northeastern China, surface soil moisture is more affected by drought indices having shorter time scales while deep-layer soil moisture is more related on longer index time scales. Multiscalar drought indices work better than drought indices from two-layer bucket models. The standardized precipitation evapotranspiration index (SPEI) works similarly or better than the standardized precipitation index (SPI) in characterizing soil moisture at different soil layers. In most stations in China, the Z index has a higher correlation with soil moisture at 0–5 cm than the Palmer drought severity index (PDSI), which in turn has a higher correlation with soil moisture at 90–100-cm depth than the Z index. Soil bulk density and soil organic carbon density are the two main soil properties affecting the spatial variations of the soil moisture–drought indices relationship. The study may facilitate agriculture drought monitoring with commonly used drought indices calculated from weather station data.


Tectonics ◽  
1996 ◽  
Vol 15 (3) ◽  
pp. 517-544 ◽  
Author(s):  
Arie J. van der Velden ◽  
Frederick A. Cook

2020 ◽  
Author(s):  
Ali Naeimi ◽  
Martin Sharp

&lt;p&gt;Under most atmospheric conditions, the albedo and temperature of surface snow and ice are two of the main influences on the energy budget for glacier melting. Given that surface albedo and temperature are linked, knowing where and when negative albedo and positive surface temperature anomalies coincide is important for identifying locations and time periods in which anomalously high rates of surface melting are likely. We used measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on NASA's AQUA and TERRA satellites&amp;#160;to map the albedo and surface temperature of snow and ice &amp;#160;on glaciers in the of Southern Rocky Mountain Trench ecoregion in the summer months (June-August)&amp;#160;from 2000 to 2018. We use these data to identify specific regions and time periods in which low albedo and high surface temperature coincide since these conditions are likely to support anomalously high rates of surface melting. We also use these data to identify regions/periods in which albedo is particularly low while surface temperature is average or low, since such conditions suggest localized and/or short-term decoupling between the two parameters. We found anomalously low albedo and average/low temperature consistently at multiple glaciers during time periods when there were major forest fire events. We suggest the low albedo results from deposition of pyrogenic carbon from forest fires. We found that, on average, ~25% of the glaciers in the region experienced increasingly negative albedo anomalies and increasingly positive temperature anomalies in summer months from 2000 to 2018. However, we also found that for ~45% of the glaciers that are small, there was a poor correlation between the timing of albedo and temperature anomalies. Our results indicate that the correlation between albedo and temperature was weaker for the small glaciers, and identify specific glaciers that are likely the most vulnerable to climate warming.&lt;/p&gt;


Sign in / Sign up

Export Citation Format

Share Document