scholarly journals Seasonal Soil Moisture Drought Prediction over Europe Using the North American Multi-Model Ensemble (NMME)

2015 ◽  
Vol 16 (6) ◽  
pp. 2329-2344 ◽  
Author(s):  
Stephan Thober ◽  
Rohini Kumar ◽  
Justin Sheffield ◽  
Juliane Mai ◽  
David Schäfer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North American ◽  
Crop Yields ◽  
Hydrologic Model ◽  
Prediction System ◽  
Model Ensemble ◽  
Entire Study ◽  
The North ◽  
Drought Prediction ◽  
The Impact

Abstract Droughts diminish crop yields and can lead to severe socioeconomic damages and humanitarian crises (e.g., famine). Hydrologic predictions of soil moisture droughts several months in advance are needed to mitigate the impact of these extreme events. In this study, the performance of a seasonal hydrologic prediction system for soil moisture drought forecasting over Europe is investigated. The prediction system is based on meteorological forecasts of the North American Multi-Model Ensemble (NMME) that are used to drive the mesoscale hydrologic model (mHM). The skill of the NMME-based forecasts is compared against those based on the ensemble streamflow prediction (ESP) approach for the hindcast period of 1983–2009. The NMME-based forecasts exhibit an equitable threat score that is, on average, 69% higher than the ESP-based ones at 6-month lead time. Among the NMME-based forecasts, the full ensemble outperforms the single best-performing model CFSv2, as well as all subensembles. Subensembles, however, could be useful for operational forecasting because they are showing only minor performance losses (less than 1%), but at substantially reduced computational costs (up to 60%). Regardless of the employed forecasting approach, there is considerable variability in the forecasting skill ranging up to 40% in space and time. High skill is observed when forecasts are mainly determined by initial hydrologic conditions. In general, the NMME-based seasonal forecasting system is well suited for a seamless drought prediction system as it outperforms ESP-based forecasts consistently over the entire study domain at all lead times.

Distributed Hydrologic Modeling in Northwest Mexico Reveals the Links between Runoff Mechanisms and Evapotranspiration

2012 ◽  
Vol 13 (3) ◽  
pp. 785-807 ◽  
Author(s):  
Agustín Robles-Morua ◽  
Enrique R. Vivoni ◽  
Alex S. Mayer
Keyword(s):  
North American ◽  
Land Surface ◽  
Temporal Variations ◽  
Hydrologic Model ◽  
Runoff Generation ◽  
Limited Region ◽  
The North ◽  
Wide Range ◽  
Infiltration Excess ◽  
The Impact

Abstract A distributed hydrologic model is used to evaluate how runoff mechanisms—including infiltration excess (RI), saturation excess (RS), and groundwater exfiltration (RG)—influence the generation of streamflow and evapotranspiration (ET) in a mountainous region under the influence of the North American monsoon (NAM). The study site, the upper Sonora River basin (~9350 km2) in Mexico, is characterized by a wide range of terrain, soil, and ecosystem conditions obtained from best available data sources. Three meteorological scenarios are compared to explore the impact of spatial and temporal variations of meteorological characteristics on land surface processes and to identify the value of North American Land Data Assimilation System (NLDAS) forcing products in the NAM region. The following scenarios are considered for a 1-yr period: 1) a sparse network of ground-based stations, 2) raw forcing products from NLDAS, and 3) NLDAS products adjusted using available station data. These scenarios are discussed in light of spatial distributions of precipitation, streamflow, and runoff mechanisms during annual, seasonal, and monthly periods. This study identified that the mode of runoff generation impacts seasonal relations between ET and soil moisture in the water-limited region. In addition, ET rates at annual and seasonal scales were related to the runoff mechanism proportions, with an increase in ET when RS was dominant and a decrease in ET when RI was more important. The partitioning of runoff mechanisms also helps explain the monthly progression of runoff ratios in these seasonally wet hydrologic systems. Understanding the complex interplay between seasonal responses of runoff mechanisms and evapotranspiration can yield information that is of interest to hydrologists and water managers.

Effects of Initial Soil Moisture on Rainfall Generation and Subsequent Hydrologic Response during the North American Monsoon

2009 ◽  
Vol 10 (3) ◽  
pp. 644-664 ◽  
Author(s):  
Enrique R. Vivoni ◽  
Kinwai Tai ◽  
David J. Gochis
Keyword(s):  
Soil Moisture ◽  
North American ◽  
Initial Conditions ◽  
Hydrologic Model ◽  
North American Monsoon ◽  
Meteorological Model ◽  
Initial Soil Moisture ◽  
The North ◽  
Spatial Coverage ◽  
Initial Soil

Abstract Through the use of a mesoscale meteorological model and distributed hydrologic model, the effects of initial soil moisture on rainfall generation, streamflow, and evapotranspiration during the North American monsoon are examined. A collection of atmospheric fields is simulated by varying initial soil moisture in the meteorological model. Analysis of the simulated rainfall fields shows that the total rainfall, intensity, and spatial coverage increase with higher soil moisture. Hydrologic simulations forced by the meteorological fields are performed using two scenarios: (i) fixed soil moisture initializations obtained via a drainage experiment in the hydrologic model and (ii) adjusted initializations to match conditions in the two models. The scenarios indicate that the runoff ratio increases with higher rainfall, although a change is observed from a linear (fixed initialization) to a nonlinear response (adjusted initialization). Variations in basin response are attributed to controls exerted by rainfall, soil, and vegetation properties for varying initial conditions. Antecedent wetness significantly influences the runoff response through the interplay of different runoff generation mechanisms and also controls the evapotranspiration process. The authors conclude that a regional increase in initial soil moisture promotes rainfall generation, streamflow, and evapotranspiration for this warm-season case study.

scholarly journals Global Meteorological Drought Prediction Using the North American Multi-Model Ensemble

2015 ◽  
Vol 16 (3) ◽  
pp. 1409-1424 ◽  
Author(s):  
Kingtse C. Mo ◽  
Bradfield Lyon
Keyword(s):  
Real Time ◽  
North American ◽  
Southern Oscillation ◽  
Value Added ◽  
Meteorological Drought ◽  
Precipitation Forecast ◽  
Monthly Precipitation ◽  
Model Ensemble ◽  
The North ◽  
Drought Prediction

Abstract Precipitation forecasts from six climate models in the North American Multi-Model Ensemble (NMME) are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for global land areas, and their skill was evaluated over the period 1982–2010. The skill of monthly precipitation forecasts from the NMME is also assessed. The value-added utility in using the NMME models to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on the inherent persistence characteristics of the SPI itself. As expected, skill of the NMME-generated SPI forecasts depends on the season, location, and specific index considered (the 3- and 6-month SPI were evaluated). In virtually all locations and seasons, statistically significant skill is found at lead times of 1–2 months, although the skill comes largely from initial conditions. Added skill from the NMME is primarily in regions exhibiting El Niño–Southern Oscillation (ENSO) teleconnections. Knowledge of the initial drought state is critical in SPI prediction, and there are considerable differences in observed SPI values between different datasets. Root-mean-square differences between datasets can exceed typical thresholds for drought, particularly in the tropics. This is particularly problematic for precipitation products available in near–real time. Thus, in the near term, the largest advances in the global prediction of meteorological drought are obtainable from improvements in near-real-time precipitation observations for the globe. In the longer term, improvements in precipitation forecast skill from dynamical models will be essential in this effort.

scholarly journals Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 172
Author(s):  
Yuan Xu ◽  
Jieming Chou ◽  
Fan Yang ◽  
Mingyang Sun ◽  
Weixing Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Food Production ◽  
Crop Yields ◽  
Climatic Factors ◽  
Climatic Data ◽  
The South ◽  
The North ◽  
Output Elasticity ◽  
The Impact

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

Vegetation controls on soil moisture distribution in the Valles Caldera, New Mexico, during the North American monsoon

Ecohydrology ◽  
2008 ◽  
Vol 1 (3) ◽  
pp. 225-238 ◽  
Author(s):  
Enrique R. Vivoni ◽  
Alex J. Rinehart ◽  
Luis A. Méndez-Barroso ◽  
Carlos A. Aragón ◽  
Gautam Bisht ◽  
...  
Keyword(s):  
Soil Moisture ◽  
New Mexico ◽  
North American ◽  
Moisture Distribution ◽  
North American Monsoon ◽  
Valles Caldera ◽  
The North ◽  
Soil Moisture Distribution

IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING

2021 ◽  
Author(s):  
SOURABH SHRIVASTAVA ◽  
RAM AVTAR ◽  
PRASANTA KUMAR BAL
Keyword(s):  
Machine Learning ◽  
North American ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Summer Monsoon Rainfall ◽  
Global Climate Models ◽  
Himalayan Region ◽  
Model Ensemble ◽  
The North

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20[Formula: see text]mm/day and an RMSE up to 15[Formula: see text]mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to [Formula: see text][Formula: see text]mm/day and RMSE up to 2[Formula: see text]mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

scholarly journals Impacts of Large-Scale Circulation on Urban Ambient Concentrations of Gaseous Elemental Mercury in New York, USA

2017 ◽  
Author(s):  
Huiting Mao ◽  
Dolly Hall ◽  
Zhuyun Ye ◽  
Ying Zhou ◽  
Dirk Felton ◽  
...  
Keyword(s):  
New York ◽  
North American ◽  
Large Scale ◽  
Measurement Data ◽  
Elemental Mercury ◽  
Urban Site ◽  
Large Scale Circulation ◽  
Gaseous Elemental Mercury ◽  
The North ◽  
The Impact

Abstract. The impact of large-scale circulation on urban gaseous elemental mercury (GEM) was investigated through analysis of 2008–2015 measurement data from an urban site in New York City (NYC), New York, USA. Distinct annual cycles were observed in 2009–2010 with mixing ratios in warm seasons (i.e. spring–summer) 10–20 ppqv (~ 10 %–25 %) higher than in cool seasons (i.e. fall–winter). This annual cycle was disrupted in 2011 by an anomalously strong influence of the North American trough in that warm season and was reproduced in 2014 with annual amplitude enhanced up to ~ 70 ppqv associated with a particularly strong Bermuda High. North American trough axis index (TAI) and intensity index (TII) were used to characterize the effect of the North American trough on NYC GEM especially in winter and summer. The intensity and position of the Bermuda High had a significant impact on GEM in warm seasons supported by a strong correlation (r reaching 0.96, p 

scholarly journals Novel Highly Pathogenic Avian A(H5N2) and A(H5N8) Influenza Viruses of Clade 2.3.4.4 from North America Have Limited Capacity for Replication and Transmission in Mammals

mSphere ◽  
2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Bryan S. Kaplan ◽  
Marion Russier ◽  
Trushar Jeevan ◽  
Bindumadhav Marathe ◽  
Elena A. Govorkova ◽  
...  
Keyword(s):  
North America ◽  
Avian Influenza ◽  
North American ◽  
Influenza Viruses ◽  
Morbidity And Mortality ◽  
Avian Influenza Viruses ◽  
Highly Pathogenic ◽  
The North ◽  
Domestic Poultry ◽  
The Impact

ABSTRACT Highly pathogenic H5 influenza viruses have been introduced into North America from Asia, causing extensive morbidity and mortality in domestic poultry. The introduced viruses have reassorted with North American avian influenza viruses, generating viral genotypes not seen on other continents. The experiments and analyses presented here were designed to assess the impact of this genetic diversification on viral phenotypes, particularly as regards mammalian hosts, by comparing the North American viruses with their Eurasian precursor viruses. Highly pathogenic influenza A(H5N8) viruses from clade 2.3.4.4 were introduced to North America by migratory birds in the fall of 2014. Reassortment of A(H5N8) viruses with avian viruses of North American lineage resulted in the generation of novel A(H5N2) viruses with novel genotypes. Through sequencing of recent avian influenza viruses, we identified PB1 and NP gene segments very similar to those in the viruses isolated from North American waterfowl prior to the introduction of A(H5N8) to North America, highlighting these bird species in the origin of reassortant A(H5N2) viruses. While they were highly virulent and transmissible in poultry, we found A(H5N2) viruses to be low pathogenic in mice and ferrets, and replication was limited in both hosts compared with those of recent highly pathogenic avian influenza (HPAI) H5N1 viruses. Molecular characterization of the hemagglutinin protein from A(H5N2) viruses showed that the receptor binding preference, cleavage, and pH of activation were highly adapted for replication in avian species and similar to those of other 2.3.4.4 viruses. In addition, North American and Eurasian clade 2.3.4.4 H5NX viruses replicated to significantly lower titers in differentiated normal human bronchial epithelial cells than did seasonal human A(H1N1) and highly pathogenic A(H5N1) viruses isolated from a human case. Thus, despite their having a high impact on poultry, our findings suggest that the recently emerging North American A(H5N2) viruses are not expected to pose a substantial threat to humans and other mammals without further reassortment and/or adaptation and that reassortment with North American viruses has not had a major impact on viral phenotype. IMPORTANCE Highly pathogenic H5 influenza viruses have been introduced into North America from Asia, causing extensive morbidity and mortality in domestic poultry. The introduced viruses have reassorted with North American avian influenza viruses, generating viral genotypes not seen on other continents. The experiments and analyses presented here were designed to assess the impact of this genetic diversification on viral phenotypes, particularly as regards mammalian hosts, by comparing the North American viruses with their Eurasian precursor viruses.

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