scholarly journals Irrigation Effects on Land–Atmosphere Coupling Strength in the United States

2017 ◽  
Vol 30 (10) ◽  
pp. 3671-3685 ◽  
Author(s):  
Yaqiong Lu ◽  
Keith Harding ◽  
Lara Kueppers
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Water Vapor ◽  
Great Plains ◽  
Coupling Strength ◽  
Climate Models ◽  
The United States ◽  
Lower Sensitivity ◽  
Local Soil ◽  
Irrigation Effects

Abstract Land–atmosphere coupling strength describes the degree to which the atmosphere responds (e.g., via changes in precipitation) to changes in the land surface state (e.g., soil moisture). The Midwest and Great Plains of the United States have been shown to be “hot spots” of coupling by many climate models and some observations. However, very few of the modeling studies have reported whether the climate models applied irrigation in the Midwest and Great Plains, where 24%–27% of farmland is irrigated, leaving open the question of whether irrigation affects current estimates of coupling strength. This study used a regional climate model that incorporated dynamic crop growth and precision irrigation (WRF3.3–CLM4crop) to investigate irrigation effects on land–atmosphere coupling strength. Coupling strength was quantified using multiple indices and the irrigated land-induced precipitation was tracked using a back trajectory method. The indices showed a consistent and significant decline in local coupling strength with irrigation in the Midwest and northern Great Plains. These reductions were due to increased soil moisture but decreased local precipitation and lower sensitivity of latent heat flux to soil moisture over irrigated regions. The back trajectories of water vapor transport confirmed that irrigation largely did not contribute to local precipitation. Water vapor from irrigated land was transported to the Midwest and U.S. Northeast where it fell as precipitation, suggesting that irrigation has a broader spatial impact on soil moisture–precipitation coupling than simply through local soil moisture–evapotranspiration coupling. The present study suggests that climate models without irrigation schemes may overestimate the land–atmosphere coupling strength over irrigated agricultural regions but underestimate coupling strength over neighboring nonirrigated regions.

scholarly journals Representation of Soil Moisture Feedbacks during Drought in NASA Unified WRF (NU-WRF)

2013 ◽  
Vol 14 (1) ◽  
pp. 360-367 ◽  
Author(s):  
Benjamin F. Zaitchik ◽  
Joseph A. Santanello ◽  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Summer Drought ◽  
Dynamic Surface ◽  
Seasonal Drought ◽  
Weather Forecasts ◽  
Local Soil

Abstract Positive soil moisture–precipitation feedbacks can intensify heat and prolong drought under conditions of precipitation deficit. Adequate representation of these processes in regional climate models is, therefore, important for extended weather forecasts, seasonal drought analysis, and downscaled climate change projections. This paper presents the first application of the NASA Unified Weather Research and Forecasting Model (NU-WRF) to simulation of seasonal drought. Simulations of the 2006 southern Great Plains drought performed with and without soil moisture memory indicate that local soil moisture feedbacks had the potential to concentrate precipitation in wet areas relative to dry areas in summer drought months. Introduction of a simple dynamic surface albedo scheme that models albedo as a function of soil moisture intensified the simulated feedback pattern at local scale—dry, brighter areas received even less precipitation while wet, whereas darker areas received more—but did not significantly change the total amount of precipitation simulated across the drought-affected region. This soil-moisture-mediated albedo land–atmosphere coupling pathway is structurally excluded from standard versions of WRF.

Assess 21st century Flash Drought in the United States using high resolution regional climate models

2021 ◽  
Author(s):  
Brandi Gamelin ◽  
Jiali Wang ◽  
V. Rao Kotamarthi
Keyword(s):  
Climate Change ◽  
United States ◽  
Soil Moisture ◽  
Climate Models ◽  
Wrf Model ◽  
The United States ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Regional Variability ◽  
Groundwater Levels

<p>Flash droughts are the rapid intensification of drought conditions generally associated with increased temperatures and decreased precipitation on short time scales.  Consequently, flash droughts are responsible for reduced soil moisture which contributes to diminished agricultural yields and lower groundwater levels. Drought management, especially flash drought in the United States is vital to address the human and economic impact of crop loss, diminished water resources and increased wildfire risk. In previous research, climate change scenarios show increased growing season (i.e. frost-free days) and drying in soil moisture over most of the United States by 2100. Understanding projected flash drought is important to assess regional variability, frequency and intensity of flash droughts under future climate change scenarios. Data for this work was produced with the Weather Research and Forecasting (WRF) model. Initial and boundary conditions for the model were supplied by CCSM4, GFDL-ESM2G, and HadGEM2-ES and based on the 8.5 Representative Concentration Pathway (RCP8.5). The WRF model was downscaled to a 12 km spatial resolution for three climate time frames: 1995-2004 (Historical), 2045-2054 (Mid), and 2085-2094 (Late).  A key characteristic of flash drought is the rapid onset and intensification of dry conditions. For this, we identify onset with vapor pressure deficit during each time frame. Known flash drought cases during the Historical run are identified and compared to flash droughts in the Mid and Late 21<sup>st</sup> century.</p>

Investigating the Relationship between the Evaporative Stress Index and Land Surface Conditions in the Contiguous United States

2020 ◽  
Vol 21 (7) ◽  
pp. 1469-1484
Author(s):  
Yafang Zhong ◽  
Jason A. Otkin ◽  
Martha C. Anderson ◽  
Christopher Hain
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
Land Surface ◽  
The United States ◽  
Stress Index ◽  
Northern Great Plains ◽  
Reference Network ◽  
North Central ◽  
Central United States

AbstractDespite the key importance of soil moisture–evapotranspiration (ET) coupling in the climate system, limited availability of soil moisture and ET observations poses a major impediment for investigation of this coupling regarding spatiotemporal characteristics and potential modifications under climate change. To better understand and quantify soil moisture–ET coupling and relevant processes, this study takes advantage of in situ soil moisture observations from the U.S. Climate Reference Network (USCRN) for the time period of 2010–17 and a satellite-derived version of the evapotranspiration stress index (ESI), which represents anomalies in a normalized ratio of actual to reference ET. The analyses reveal strong seasonality and regional characteristics of the ESI–land surface interactions across the United States, with the strongest control of soil moisture on the ESI found in the southern Great Plains during spring, and in the north-central United States, the northern Great Plains, and the Pacific Northwest during summer. In drier climate regions such as the northern Great Plains and north-central United States, soil moisture control on the ESI is confined to surface soil layers, with subsurface soil moisture passively responding to changes in the ESI. The soil moisture–ESI interaction is more uniform between surface and subsurface soils in wetter regions with higher vegetation cover. These results provide a benchmark for simulation of soil moisture–ET coupling and are useful for projection of associated climate processes in the future.

scholarly journals Whither the 100th Meridian? The Once and Future Physical and Human Geography of America’s Arid–Humid Divide. Part II: The Meridian Moves East

2018 ◽  
Vol 22 (5) ◽  
pp. 1-24 ◽  
Author(s):  
Richard Seager ◽  
Jamie Feldman ◽  
Nathan Lis ◽  
Mingfang Ting ◽  
Alton P. Williams ◽  
...  
Keyword(s):  
United States ◽  
Great Plains ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Coupled Model ◽  
Moisture Flux ◽  
The United States ◽  
Farm Size ◽  
Current Century ◽  
Northern United States

Abstract The 100th meridian bisects the Great Plains of the United States and effectively divides the continent into more arid western and less arid eastern halves and is well expressed in terms of vegetation, land hydrology, crops, and the farm economy. Here, it is considered how this arid–humid divide will change in intensity and location during the current century under rising greenhouse gases. It is first shown that state-of-the-art climate models from phase 5 of the Coupled Model Intercomparison Project generally underestimate the degree of aridity of the United States and simulate an arid–humid divide that is too diffuse. These biases are traced to excessive precipitation and evapotranspiration and inadequate blocking of eastward moisture flux by the Pacific coastal ranges and Rockies. Bias-corrected future projections are developed that modify observationally based measures of aridity by the model-projected fractional changes in aridity. Aridity increases across the United States, and the aridity gradient weakens. The main contributor to the changes is rising potential evapotranspiration, while changes in precipitation working alone increase aridity across the southern and decrease across the northern United States. The “effective 100th meridian” moves to the east as the century progresses. In the current farm economy, farm size and percent of county under rangelands increase and percent of cropland under corn decreases as aridity increases. Statistical relations between these quantities and the bias-corrected aridity projections suggest that, all else being equal (which it will not be), adjustment to changing environmental conditions would cause farm size and rangeland area to increase across the plains and percent of cropland under corn to decrease in the northern plains as the century advances.

scholarly journals Estimating root zone soil moisture using near-surface observations from SMOS

2014 ◽  
Vol 18 (1) ◽  
pp. 139-154 ◽  
Author(s):  
T. W. Ford ◽  
E. Harris ◽  
S. M. Quiring
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
Surface Soil ◽  
Root Zone ◽  
The United States ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Cross Correlation Analysis

Abstract. Satellite-derived soil moisture provides more spatially and temporally extensive data than in situ observations. However, satellites can only measure water in the top few centimeters of the soil. Root zone soil moisture is more important, particularly in vegetated regions. Therefore estimates of root zone soil moisture must be inferred from near-surface soil moisture retrievals. The accuracy of this inference is contingent on the relationship between soil moisture in the near-surface and the soil moisture at greater depths. This study uses cross correlation analysis to quantify the association between near-surface and root zone soil moisture using in situ data from the United States Great Plains. Our analysis demonstrates that there is generally a strong relationship between near-surface (5–10 cm) and root zone (25–60 cm) soil moisture. An exponential decay filter is used to estimate root zone soil moisture using near-surface soil moisture derived from the Soil Moisture and Ocean Salinity (SMOS) satellite. Root zone soil moisture derived from SMOS surface retrievals is compared to in situ soil moisture observations in the United States Great Plains. The SMOS-based root zone soil moisture had a mean R2 of 0.57 and a mean Nash–Sutcliffe score of 0.61 based on 33 stations in Oklahoma. In Nebraska, the SMOS-based root zone soil moisture had a mean R2 of 0.24 and a mean Nash–Sutcliffe score of 0.22 based on 22 stations. Although the performance of the exponential filter method varies over space and time, we conclude that it is a useful approach for estimating root zone soil moisture from SMOS surface retrievals.

scholarly journals Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data

2020 ◽  
Vol 12 (20) ◽  
pp. 3434
Author(s):  
Maryia Halubok ◽  
Zong-Liang Yang
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Chlorophyll Fluorescence ◽  
Spatial Variability ◽  
Great Plains ◽  
Gross Primary Production ◽  
European Space Agency ◽  
The United States ◽  
Soil Moisture Data ◽  
The Us

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m2/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates.

Biennial Wormwood (Artemisia biennis) Early-Season Control with Herbicides

2004 ◽  
Vol 18 (3) ◽  
pp. 611-618 ◽  
Author(s):  
Bradley E. Fronning ◽  
George O. Kegode
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Great Plains ◽  
The United States ◽  
Northern Great Plains ◽  
Early Season ◽  
Herbicide Treatments ◽  
Moisture Conditions

Biennial wormwood has become a problem for soybean producers in the northern Great Plains of the United States. Research was conducted to evaluate control of biennial wormwood with preemergence (PRE) herbicides alone or followed by postemergence (POST) herbicides in 2000 and 2001 at Fargo, Leonard, and Wyndmere, ND. Favorable soil moisture conditions at Leonard resulted in continual emergence and greater densities of biennial wormwood, whereas the soil at Fargo and Wyndmere was dry and few biennial wormwood seedlings emerged at these locations. Biennial wormwood control with PRE herbicides was greater than 89% at Fargo and Wyndmere but was 80% or lower at Leonard. PRE biennial wormwood control was higher with flumetsulam than with sulfentrazone. When POST treatments were applied after PRE herbicides, biennial wormwood control 4 wk after treatment was 92% or better at Fargo and Wyndmere but was 76% or less at Leonard. The combination of PRE and POST herbicide treatments did not improve control greatly at Fargo or Wyndmere but at Leonard reduced the number of biennial wormwood plants.

scholarly journals Summer Land–Atmosphere Coupling Strength in the United States: Comparison among Observations, Reanalysis Data, and Numerical Models

2012 ◽  
Vol 13 (3) ◽  
pp. 1010-1022 ◽  
Author(s):  
Rui Mei ◽  
Guiling Wang
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Coupling Strength ◽  
Numerical Models ◽  
The United States ◽  
Reanalysis Data ◽  
Infiltration Capacity ◽  
Community Land Model ◽  
Model Version

Abstract This study examines the land–atmosphere coupling strength during summer over subregions of the United States based on observations [Climate Prediction Center (CPC)–Variable Infiltration Capacity (VIC)], reanalysis data [North American Regional Reanalysis (NARR) and NCEP Climate Forecast System Reanalysis (CFSR)], and models [Community Atmosphere Model, version 3 (CAM3)–Community Land Model, version 3 (CLM3) and CAM4–CLM4]. The probability density function of conditioned correlation between soil moisture and subsequent precipitation or surface temperature during the years of large precipitation anomalies is used as a measure for the coupling strength. There are three major findings: 1) among the eight subregions (classified by land cover types), the transition zone Great Plains (and, to a lesser extent, the Midwest and Southeast) are identified as hot spots for strong land–atmosphere coupling; 2) soil moisture–precipitation coupling is weaker than soil moisture–surface temperature coupling; and 3) the coupling strength is stronger in observational and reanalysis products than in the models examined, especially in CAM4–CLM4. The conditioned correlation analysis also indicates that the coupling strength in CAM4–CLM4 is weaker than in CAM3–CLM3, which is further supported by Global Land–Atmosphere Coupling Experiments1 (GLACE1)-type experiments and attributed to changes in CAM rather than modifications in CLM. Contrary to suggestions in previous studies, CAM–CLM models do not seem to overestimate the land–atmosphere coupling strength.

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