scholarly journals Ensemble forecast spread induced by soil moisture changes over mid-south and neighbouring mid-western region of the USA

2012 ◽  
Vol 64 (1) ◽  
pp. 17156 ◽  
Author(s):  
ArturoI. Quintanar ◽  
Rezaul Mahmood
Keyword(s):  
Soil Moisture ◽  
Ensemble Forecast ◽  
Western Region ◽  
The Usa

Drought spatiotemporal propagation via land feedbacks

2021 ◽  
Author(s):  
Diego G. Miralles ◽  
Dominik L. Schumacher ◽  
Jessica Keune ◽  
Paul A. Dirmeyer
Keyword(s):  
Soil Moisture ◽  
Water Vapor ◽  
Mitigation Strategies ◽  
Soil Drought ◽  
The Usa ◽  
Forecast Models ◽  
Atmospheric Feedbacks ◽  
Dry Out ◽  
Water And Food Security ◽  
The Impact

<p>The predicted increase in drought occurrence and intensity will pose serious threats to global future water and food security. This was hinted by several historically unprecedented droughts over the last two decades, taking place in Europe, Australia, Amazonia or the USA. It has been hypothesised that the strength of these events responded to self-reinforcement processes related to land–atmospheric feedbacks: as rainfall deficits dry out soil and vegetation, the evaporation of land water is reduced, then the local air becomes too dry to yield rainfall, which further enhances drought conditions. Despite the 'local' nature of these feedbacks, their consequences can be remote, as downwind regions may rely on evaporated water transported by winds from drought-affected locations. Following this rationale, droughts may not only self-reinforce locally, due to land atmospheric feedbacks, but <em>self-propagate</em> in the downwind direction, always conditioned on atmospheric circulation. This propagation is not only meteorological but relies on soil moisture drought, and may lead to a downwind cascading of impacts on water resources. However, a global capacity to observe these processes is lacking, and thus our knowledge of how droughts start and evolve, and how this may change as climate changes, remains limited. Furthermore, climate and forecast models are still immature when it comes to representing the influences of land on rainfall.</p><p>Here, the largest global drought events are studied to unravel the role of land–atmosphere feedbacks during the spatiotemporal propagation of these events. We based our study on satellite and reanalysis records of soil moisture, evaporation, air humidity, winds and precipitation, in combination with a Lagrangian framework that can map water vapor trajectories and explore multi-dimensional feedbacks. We estimate the reduction in precipitation in the direction of drought propagation that is caused by the upwind soil moisture drought, and isolate this effect from the influence of potential evaporation and circulation changes. By doing so, the downwind lack of precipitation caused by upwind soil drought via water vapor deficits, and hence the impact of drought self-propagation, is determined. We show that droughts occurring in dryland regions are particularly prone to self-propagate, as evaporation there tends to respond strongly to enhanced soil stress and precipitation is frequently convective. This kind of knowledge may be used to improve climate and forecast models and can be exploited to develop geo-engineering mitigation strategies to help prevent drought events from aggravating during their early stages.</p>

scholarly journals Retrospective analysis of feline intestinal parasites: trends in testing positivity by age, USA geographical region and reason for veterinary visit

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Sarah Sweet ◽  
Donald Szlosek ◽  
Donald McCrann ◽  
Michael Coyne ◽  
David Kincaid ◽  
...  
Keyword(s):  
Intestinal Parasites ◽  
Geographical Region ◽  
Reference Laboratory ◽  
Western Region ◽  
Test Results ◽  
True Prevalence ◽  
Increased Risk ◽  
Parasite Detection ◽  
The Usa ◽  
Fecal Tests

Abstract Background The goals of this retrospective study were to estimate parasite positivity in samples from cats using zinc sulfate fecal flotation by centrifugation (“centrifugation”) and coproantigen and examine trends with age, geographical region and reason for visit to veterinarian. Common methods of parasite detection, such as centrifugal flotation, passive flotation, or direct smear, may underrepresent the true prevalence of intestinal parasites in cats. Coproantigen testing detects more positive samples than traditional methods alone. Methods Feline fecal test results from the continental USA containing results for fecal exams performed using centrifugation paired with coproantigen results for ascarid, hookworm, whipworm and Giardia were obtained from the database of a national commercial reference laboratory comprised of multiple regional sites. Results Parasite positivity was highest in samples from young cats and decreased with cat age. The western region of the USA had lower total parasite positivity than other regions for all parasites except Giardia. Cats receiving fecal tests during veterinary wellness visits had only slightly lower parasite positivity than samples from cats during sick clinical visits. Conclusions This study showed a larger population of cats are at increased risk of parasitism than commonly believed and coproantigen testing produces more positive test results for the four parasites that antigen can detect than centrifugation of feline fecal samples.

scholarly journals Utility of Global Ensemble Forecast System (GEFS) Reforecast for Medium-Range Drought Prediction in India

2016 ◽  
Vol 17 (6) ◽  
pp. 1781-1800 ◽  
Author(s):  
Reepal D. Shah ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Monsoon Season ◽  
Ensemble Forecast ◽  
Extreme Weather Events ◽  
Forecast System ◽  
Drought Prediction ◽  
Air Temperatures ◽  
Medium Range ◽  
Precipitation And Temperature

Abstract Medium-range (~7 days) forecasts of agricultural and hydrologic droughts can help in decision-making in agriculture and water resources management. India has witnessed severe losses due to extreme weather events during recent years and medium-range forecasts of precipitation, air temperatures (maximum and minimum), and hydrologic variables (root-zone soil moisture and runoff) can be valuable. Here, the skill of the Global Ensemble Forecast System (GEFS) reforecast of precipitation and air temperatures is evaluated using retrospective data for the period of 1985–2010. It is found that the GEFS forecast shows better skill in the nonmonsoon season than in the monsoon season in India. Moreover, skill in temperature forecast is higher than that of precipitation in both the monsoon and nonmonsoon seasons. The lower skill in forecasting precipitation during the monsoon season can be attributed to representation of intraseasonal variability in precipitation from the GEFS. Among the selected regions, the northern, northeastern, and core monsoon region showed relatively lower skill in the GEFS forecast. Temperature and precipitation forecasts were corrected from the GEFS using quantile–quantile (Q–Q) mapping and linear scaling, respectively. Bias-corrected forecasts for precipitation and air temperatures were improved over the raw forecasts. The influence of corrected and raw forcings on medium-range soil moisture, drought, and runoff forecasts was evaluated. The results showed that because of high persistence, medium-range soil moisture forecasts are largely determined by the initial hydrologic conditions. Bias correction of precipitation and temperature forecasts does not lead to significant improvement in the medium-range hydrologic forecasting of soil moisture and drought. However, bias correcting raw GEFS forecasts can provide better predictions of the forecasts of precipitation and temperature anomalies over India.

scholarly journals Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on Linear Geostatistics

2019 ◽  
Author(s):  
Ricardo M. Llamas ◽  
Mario Guevara ◽  
Danny Rorabaugh ◽  
Michela Taufer ◽  
Rodrigo Vargas
Keyword(s):  
Soil Moisture ◽  
Spatial Data ◽  
Missing Values ◽  
Linear Models ◽  
European Space Agency ◽  
Correlation Coefficients ◽  
Ground Truth ◽  
Frozen Soil ◽  
Ground Truth Data ◽  
The Usa

Soil moisture plays a key role in the Earth’s water and carbon cycles, but acquisition of continuous (i.e., gap-free) soil moisture measurements across large regions is a challenging task due to limitations of currently available point measurements. Satellites offer critical information for soil moisture over large areas on a regular basis (e.g., ESA CCI, NASA SMAP), however, there are regions where satellite-derived soil moisture cannot be estimated because of certain circumstances such as high canopy density, frozen soil, or extreme dry conditions. We compared and tested two approaches--Ordinary Kriging (OK) interpolation and General Linear Models (GLM)--to model soil moisture and fill spatial data gaps from the European Space Agency Climate Change Initiative (ESA CCI) version 3.2 (and compared them with version 4.4) from January 2000 to September 2012, over a region of 465,777 km2 across the Midwest of the USA. We tested our proposed methods to fill gaps in the original ESA CCI product, and two data subsets, removing 25% and 50% of the initially available valid pixels. We found a significant correlation coefficient (r = 0.523, RMSE = 0.092 m3m-3) between the original satellite-derived soil moisture product with ground-truth data from the North American Soil Moisture Database (NASMD). Predicted soil moisture using OK also had significant correlation coefficients with NASMD data, when using 100% (r = 0.522, RMSE = 0.092 m3m-3), 75% (r = 0.526, RMSE = 0.092 m3m-3) and 50% (r = 0.53, RMSE = 0.092 m3m-3) of available valid pixels for each month of the study period. GLM had lower but significant correlation coefficients with NASMD data (average r = 0.478, RMSE = 0.092 m3m-3) when using the same subsets of available data (i.e., 100%, 75%, 50%). Our results provide support for OK as a technique to gap-fill spatial missing values of satellite-derived soil moisture products across the Midwest of the USA.

INTER-SEASONAL AND INTER-BASINS HYDROLOGICAL COUPLING IN SOUTH AMERICA

2021 ◽  
Author(s):  
Paulo Rodrigo Zanin ◽  
Prakki Satyamurty
Keyword(s):  
Soil Moisture ◽  
Amazon Basin ◽  
Winter Season ◽  
Western Region ◽  
South American ◽  
La Plata ◽  
La Plata Basin ◽  
Precipitation Recycling ◽  
Source Sink ◽  
Southeastern Region

AbstractThe inter-seasonal and inter-basins hydrological couplings between the Amazon and the La Plata basins are obtained with the help of ERA-5 atmospheric reanalysis, MERGE/CPTEC precipitation, GLEAM evapotranspiration and the GLDAS/Noah soil moisture datasets. The hypotheses formulated by Zanin and Satyamurty (2020a) about the hydrological processes interconnecting the Amazon Basin and the La Plata Basin are tested. A new method for finding the source-sink relationships among the boxes (regions) is presented. The precipitation recycling, frequency of source-sink behaviors, the soil moisture memory and the continental moisture transport between remote regions are evaluated. The main result of this study is that the amount of water precipitated over the Southeastern region of the Amazon Basin at the end of the South American Monsoon during autumn season, influences the amount of precipitation during winter season over the Central-western region of the La Plata Basin.

scholarly journals Land Surface Parameter and State Perturbations in the Global Ensemble Forecast System

2019 ◽  
Vol 147 (4) ◽  
pp. 1319-1340
Author(s):  
Maria Gehne ◽  
Thomas M. Hamill ◽  
Gary T. Bates ◽  
Philip Pegion ◽  
Walter Kolczynski
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Systematic Bias ◽  
Perturbation Scheme ◽  
Forecast System ◽  
Vegetation Fraction ◽  
Land Surface Parameter

Abstract The National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) is underdispersive near the surface, a common characteristic of ensemble prediction systems. Here, several methods for increasing the spread are tested, including perturbing soil initial conditions, soil tendencies, and surface parameters, with physically based perturbations. Perturbations are applied to the soil initial conditions based on empirical orthogonal functions (EOFs) of differences between normalized soil moisture states from two land surface models (LSMs). Perturbations to roughness lengths for heat and momentum, soil hydraulic conductivity, stomatal resistance, vegetation fraction, and albedo are applied, with the amplitude and perturbation scales based on previous research. Soil moisture and temperature tendencies are also perturbed using a stochastic perturbation scheme. The results show that surface perturbations, through their impact on 2-m temperature spread, have a modest positive impact on the skill of short-range ensemble forecasts. However, adjusting the forecasts using an estimate of the systematic bias shows that bias correction has a greater impact on the forecast reliability than surface perturbations, indicating that systematic bias in the model needs to be addressed as well.

scholarly journals The importance of management information and soil moisture representation for simulating tillage effects on N<sub>2</sub>O emissions in LPJmL5.0-tillage

2020 ◽  
Author(s):  
Femke Lutz ◽  
Stephen DelGrosso ◽  
Stephen Ogle ◽  
Stephen Williams ◽  
Sara Minoli ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Meta Analysis ◽  
Agricultural Management ◽  
Water Dynamics ◽  
N2o Emissions ◽  
No Tillage ◽  
Emissions Modeling ◽  
The Usa ◽  
Global Estimates

Abstract. No-tillage is often suggested as a strategy to reduce greenhouse gas emissions. Modeling tillage effects on nitrous oxide (N2O) emissions is challenging and subject to large uncertainties, as the processes producing the emissions are complex and strongly non-linear. Previous findings have shown deviations between the LPJmL5.0-tillage model and results from meta-analysis on global estimates of tillage effects on N2O emissions. Here we tested LPJmL5.0-tillage at four different experimental sites across Europe and the USA, to verify whether deviations in N2O emissions under different tillage regimes result from a lack of detailed information on agricultural management and/or the representation of soil water dynamics. Model results were compared to observational data and outputs from field-scale DayCent simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer data base for comparison than non-continuous measurements at the experimental sites. We found that adding information on agricultural management improved the simulation of tillage effects on N2O emissions in LPJmL. We also found that LPJmL overestimated N2O emissions as well as the effects of no-tillage on N2O emissions, whereas DayCent tended to underestimate the emissions of no-tillage treatments. LPJmL showed a general bias to over-estimate soil moisture content. Modifications of hydraulic properties in LPJmL in order to match properties assumed in DayCent, as well as of the parameters related to residue cover, improved the overall simulation of soil water as well as the N2O emissions simulated under tillage and no-tillage separately. However, the effects of no-tillage (shifting from tillage to no-tillage) did not improve. Advancing the current state of information on agricultural management as well as improvements in soil moisture highlight the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions.

scholarly journals The importance of management information and soil moisture representation for simulating tillage effects on N<sub>2</sub>O emissions in LPJmL5.0-tillage

2020 ◽  
Vol 13 (9) ◽  
pp. 3905-3923
Author(s):  
Femke Lutz ◽  
Stephen Del Grosso ◽  
Stephen Ogle ◽  
Stephen Williams ◽  
Sara Minoli ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Meta Analysis ◽  
Agricultural Management ◽  
Water Dynamics ◽  
N2o Emissions ◽  
No Tillage ◽  
The Usa ◽  
Global Estimates ◽  
Daycent Model

Abstract. No-tillage is often suggested as a strategy to reduce greenhouse gas emissions. Modeling tillage effects on nitrous oxide (N2O) emissions is challenging and subject to great uncertainties as the processes producing the emissions are complex and strongly nonlinear. Previous findings have shown deviations between the LPJmL5.0-tillage model (LPJmL: Lund–Potsdam–Jena managed Land) and results from meta-analysis on global estimates of tillage effects on N2O emissions. Here we tested LPJmL5.0-tillage at four different experimental sites across Europe and the USA to verify whether deviations in N2O emissions under different tillage regimes result from a lack of detailed information on agricultural management, the representation of soil water dynamics or both. Model results were compared to observational data and outputs from field-scale DayCent model simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer database for comparison than noncontinuous measurements at experimental sites. We found that adding information on agricultural management improved the simulation of tillage effects on N2O emissions in LPJmL. We also found that LPJmL overestimated N2O emissions and the effects of no-tillage on N2O emissions, whereas DayCent tended to underestimate the emissions of no-tillage treatments. LPJmL showed a general bias to overestimate soil moisture content. Modifications of hydraulic properties in LPJmL in order to match properties assumed in DayCent, as well as of the parameters related to residue cover, improved the overall simulation of soil water and N2O emissions simulated under tillage and no-tillage separately. However, the effects of no-tillage (shifting from tillage to no-tillage) did not improve. Advancing the current state of information on agricultural management and improvements in soil moisture highlights the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions.

Will Perturbing Soil Moisture Improve Warm-Season Ensemble Forecasts? A Proof of Concept

2006 ◽  
Vol 134 (11) ◽  
pp. 3174-3189 ◽  
Author(s):  
Christian Sutton ◽  
Thomas M. Hamill ◽  
Thomas T. Warner
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface ◽  
Short Range ◽  
Wrf Model ◽  
Warm Season ◽  
Ensemble Forecast ◽  
Grid Spacing ◽  
Ensemble Forecasts ◽  
Convective Parameterization

Abstract Current generation short-range ensemble forecast members tend to be unduly similar to each other, especially for components such as surface temperature and precipitation. One possible cause of this is a lack of perturbations to the land surface state. In this experiment, a two-member ensemble of the Advanced Research Weather Research and Forecasting (WRF) model (ARW) was run from two different soil moisture analyses. One-day forecasts were conducted for six warm-season cases over the central United States with moderate soil moistures, both with explicit convection at 5-km grid spacing and with parameterized convection at 20-km grid spacing. Since changing the convective parameterization has previously been demonstrated to cause significant differences between ensemble forecast members, 20-km simulations were also conducted that were initialized with the same soil moisture but that used two different convective parameterizations as a reference. At 5 km, the forecast differences due to changing the soil moisture were comparable to the differences in 20-km simulations with the same soil moisture but with a different convective parameterization. The differences of 20-km simulations from different soil moistures were occasionally large but typically smaller than the differences from changing the convective parameterization. Thus, perturbing the state of the land surface for this version of WRF/ARW was judged to be likely to increase the spread of warm-season operational short-range ensemble forecasts of precipitation and surface temperature when soil moistures are moderate in value, especially if the ensemble is comprised of high-resolution members with explicit convection.

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