Seasonal forecast of agricultural impacts of droughts in Mexico through a principal component regression based approach

2020 ◽  
Author(s):  
Roberto Real-Rangel ◽  
Adrián Pedrozo-Acuña ◽  
Agustín Breña-Naranjo
Keyword(s):  
Soil Moisture ◽  
Regional Scale ◽  
Principal Component Regression ◽  
Principal Component ◽  
Drought Monitoring ◽  
Drought Forecasting ◽  
Moisture Index ◽  
The North ◽  
Wide Range ◽  
Monitoring And Forecasting

<p>Drought monitoring and forecasting allows to adopt mitigating actions in early stages of an event to reduce the vulnerability of a wide range of environmetal, economical and social sectors. In Mexico, various drought monitoring systems on national and regional scale perform a follow up of these events, such as the Drought Monitor in Mexico, and the North American Drought Monitor, but seasonal drought forecasting is still a pending task. This study aims at fill this gap applying a methodology that uses data derived from a globally available atmospheric reanalysis product and a principal component regression based model oriented to predict drought impacts in rainfed crops associated to deficits in the soil moisture, estimated by means of the standardized soil moisture index (SSI). Using the state of Guanajuato (Center-North of Mexico) as a study case, the model generated yielded RSME values of 0.74 using regional and global hydrological, climatic and atmospheric variables as predictors with a lead-time of 4 months.</p>

scholarly journals Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1564 ◽  
Author(s):  
Melanie Oertel ◽  
Francisco Meza ◽  
Jorge Gironás ◽  
Christopher A. Scott ◽  
Facundo Rojas ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Drought Monitoring ◽  
Moisture Index ◽  
Data Set ◽  
Basin Scale ◽  
Soil Moisture Index ◽  
Streamflow Data ◽  
Governmental Institutions ◽  
Negative Impacts ◽  
Combined Data

Detecting droughts as early as possible is important in avoiding negative impacts on economy, society, and environment. To improve drought monitoring, we studied drought propagation (i.e., the temporal manifestation of a precipitation deficit on soil moisture and streamflow). We used the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Streamflow Index (SSI), and Standardized Soil Moisture Index (SSMI) in three drought-prone regions: Sonora (Mexico), Maipo (Chile), and Mendoza-Tunuyán (Argentina) to study their temporal interdependence. For this evaluation we use precipitation, temperature, and streamflow data from gauges that are managed by governmental institutions, and satellite-based soil moisture from the ESA CCI SM v03.3 combined data set. Results confirm that effective drought monitoring should be carried out (1) at river-basin scale, (2) including several variables, and (3) considering hydro-meteorological processes from outside its boundaries.

Factors affecting interannual variation in growth of western Canadian aspen forests during 1951-2000

2005 ◽  
Vol 35 (3) ◽  
pp. 610-622 ◽  
Author(s):  
EH (Ted) Hogg ◽  
James P Brandt ◽  
B Kochtubajda
Keyword(s):  
Populus Tremuloides ◽  
Interannual Variation ◽  
Regional Scale ◽  
Forest Health ◽  
Basal Area ◽  
Deciduous Tree ◽  
Severe Drought ◽  
Moisture Index ◽  
Canadian Prairies ◽  
The North

Trembling aspen (Populus tremuloides Michx.) is the most important deciduous tree in the North American boreal forest and is also the dominant tree in the aspen parkland zone along the northern edge of the Canadian prairies. Since the 1990s, observations of dieback and reduced growth of aspen forests have led to concerns about the potential impacts of climate change. To address these concerns, a regional-scale study (CIPHA) was established in 2000 that includes annual monitoring of forest health and productivity of 72 aspen stands across the western Canadian interior. Tree-ring analysis was conducted to determine the magnitude and cause of temporal variation in stand growth of aspen at the scale (1800 km × 500 km area) encompassed by this study. The results showed that during 1951–2000 the region's aspen forests underwent several cycles of reduced growth, notably between 1976 and 1981, when mean stand basal area increment decreased by about 50%. Most of the growth variation was explained by interannual variation in a climate moisture index in combination with insect defoliation. The results of the analysis indicate that a major collapse in aspen productivity likely occurred during the severe drought that affected much of the region during 2001–2003.

scholarly journals Characterizing Soil Physical Properties for Soil Moisture Monitoring with the North Carolina Environment and Climate Observing Network

2012 ◽  
Vol 29 (7) ◽  
pp. 933-943 ◽  
Author(s):  
Weinan Pan ◽  
R. P. Boyles ◽  
J. G. White ◽  
J. L. Heitman
Keyword(s):  
North Carolina ◽  
Soil Moisture ◽  
Physical Properties ◽  
Soil Properties ◽  
Water Content ◽  
Bulk Density ◽  
Soil Physical Properties ◽  
The North ◽  
Wide Range ◽  
Soil Moisture Monitoring

Abstract Soil moisture has important implications for meteorology, climatology, hydrology, and agriculture. This has led to growing interest in development of in situ soil moisture monitoring networks. Measurement interpretation is severely limited without soil property data. In North Carolina, soil moisture has been monitored since 1999 as a routine parameter in the statewide Environment and Climate Observing Network (ECONet), but with little soils information available for ECONet sites. The objective of this paper is to provide soils data for ECONet development. The authors studied soil physical properties at 27 ECONet sites and generated a database with 13 soil physical parameters, including sand, silt, and clay contents; bulk density; total porosity; saturated hydraulic conductivity; air-dried water content; and water retention at six pressures. Soil properties were highly variable among individual ECONet sites [coefficients of variation (CVs) ranging from 12% to 80%]. This wide range of properties suggests very different behavior among sites with respect to soil moisture. A principal component analysis indicated parameter groupings associated primarily with soil texture, bulk density, and air-dried water content accounted for 80% of the total variance in the dataset. These results suggested that a few specific soil properties could be measured to provide an understanding of differences in sites with respect to major soil properties. The authors also illustrate how the measured soil properties have been used to develop new soil moisture products and data screening for the North Carolina ECONet. The methods, analysis, and results presented here have applications to North Carolina and for other regions with heterogeneous soils where soil moisture monitoring is valuable.

scholarly journals Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 705 ◽  
Author(s):  
Haekyung Park ◽  
Kyungmin Kim ◽  
Dong kun Lee
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Meteorological Data ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Absolute Error ◽  
Severe Drought ◽  
Drought Forecasting ◽  
Moisture Index ◽  
Training Area

The uncertainty of drought forecasting based on past meteorological data is increasing because of climate change. However, agricultural droughts, associated with food resources and determined by soil moisture, must be predicted several months ahead for timely resource allocation. Accordingly, we designed a severe drought area prediction (SDAP) model for short-term drought without meteorological data. The predictions of our proposed SDAP model indicate a forecast of serious drought areas assuming non-rainfall, not a probability prediction of drought occurrence. Furthermore, this prediction provides more practical information to help with rapid water allocation during a real drought. The model structure using remote sensing data consists of two parts. First, the drought function f(x) from the training area by random forest (RF) learned the changes in the pattern of soil moisture index (SMI) from the past drought and the training performance was found to be root mean square error (RMSE) = 0.052, mean absolute error (MAE) = 0.039, R2 = 0.91. Second, derived f(x) predicted the SMI of the study area, which is 20 times larger than the training area, of the same season of another year as RMSE = 0.382, MAE = 0.375, R2 = 0.58. We also obtained the variable importance stemming from RF and discussed its meaning along with the advantages and limitations of the model, training areas selection, and prediction coverage.

scholarly journals Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China

2018 ◽  
Vol 10 (8) ◽  
pp. 1302 ◽  
Author(s):  
Jueying Bai ◽  
Qian Cui ◽  
Deqing Chen ◽  
Haiwei Yu ◽  
Xudong Mao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Water Deficit ◽  
Microwave Remote Sensing ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
In Situ Observation ◽  
Soil Water Deficit ◽  
The South ◽  
The North

China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, the evaluation of Soil Moisture Active Passive (SMAP) SM products is inadequate, and L-band microwave data have not been applied to agricultural drought monitoring throughout China. In this study, first, we provide a pivotal evaluation of the SMAP L3 radiometer-derived SM product using in situ observation data throughout China, to assist in subsequent drought assessment, and then the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) is compared with the atmospheric water deficit (AWD) and vegetation health index (VHI). It is found that the SMAP can obtain SM with relatively high accuracy and the SWDI-SMAP has a good overall performance on drought monitoring. Relatively good performance of SWDI-SMAP is shown, except in some mountain regions; the SWDI-SMAP generally performs better in the north than in the south for less dry bias, although better performance of SMAP SM based on the R is shown in the south than in the north; differences between the SWDI-SMAP and VHI are mainly shown in areas without vegetation or those containing drought-resistant plants. In summary, the SWDI-SMAP shows great application potential in drought monitoring.

Uncovering the Canning Basin: a new comprehensive SEEBASE® model

2018 ◽  
Vol 58 (2) ◽  
pp. 793
Author(s):  
Karen Connors ◽  
Cedric Jorand ◽  
Peter Haines ◽  
Yijie Zhan ◽  
Lynn Pryer
Keyword(s):  
Western Australia ◽  
Potential Field ◽  
Field Data ◽  
Regional Scale ◽  
Structural Evolution ◽  
Potential Field Data ◽  
Canning Basin ◽  
The North ◽  
Wide Range ◽  
Order Of Magnitude

A new regional scale SEEBASE® model has been produced for the intracratonic Canning Basin, located in the north of Western Australia. The 2017 Canning Basin SEEBASE model is more than an order of magnitude higher resolution than the 2005 OZ SEEBASE version — the average resolution is ~1 : 1 M scale with higher resolution in areas of shallow basement with 2D seismic coverage — such as the Broome Platform and Barbwire Terrace. Post-2005 acquisition of potential field, seismic and well data in the Canning Basin by the Geological Survey of Western Australia (GSWA), Geoscience Australia and industry provided an excellent opportunity to upgrade the SEEBASE depth-to-basement model in 2017. The SEEBASE methodology focuses on a regional understanding of basement, using potential field data to interpret basement terranes, depth-to-basement (SEEBASE), regional structural geology and basement composition. The project involved extensive potential field processing and enhancement and compilation of a wide range of datasets. Integrated interpretation of the potential field data with seismic and well analysis has proven quite powerful and illustrates the strong basement control on the extent and location of basin elements. The project has reassessed the structural evolution of the basin, identified and mapped major structures and produced fault-event maps for key tectonic events. In addition, interpretative maps of basement terranes, depth-to-Moho, basement thickness, basement composition and total sediment thickness have been used to calculate a basin-wide map of basement-derived heat flow. The 2017 Canning Basin SEEBASE is the first public update of the widely used 2005 OZ SEEBASE. All the data and interpretations are available from the GSWA as a report and integrated ArcGIS project, which together provide an excellent summary of the key features within the Canning Basin that will aid hydrocarbon and mineral explorers in the region.

scholarly journals Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 866 ◽  
Author(s):  
Myriam Foucras ◽  
Mehrez Zribi ◽  
Clément Albergel ◽  
Nicolas Baghdadi ◽  
Jean-Christophe Calvet ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Sensors ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Climatic Conditions ◽  
Optical Data ◽  
Surface Soil Moisture ◽  
Moisture Index ◽  
Wide Range ◽  
Moderate Resolution Imaging Spectroradiometer

The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, applied to a series of measurements over a three-year period (2015 to 2018). The algorithm described here as “Soil Moisture Estimations from the Synergy of Sentinel-1 and optical sensors (SMES)” proposes different options, allowing information from vegetation densities and seasonal conditions to be taken into account. The output from this algorithm is a moisture index ranging between 0 and 1, with 0 corresponding to the driest soils and 1 to the wettest soils. This methodology has been tested at different test sites (South of France, Central Tunisia, Western Benin and Southwestern Niger), characterized by a wide range of different climatic conditions. The resulting surface soil moisture estimations are compared with in situ measurements and already existing satellite-derived soil moisture ASCAT (Advanced SCATterometer) products. They are found to be well correlated, for the African regions in particular (RMSE below 6 vol.%). This outcome indicates that the proposed algorithm can be used with confidence to estimate the surface soil moisture of a wide range of climatically different sites.

scholarly journals Analysis of the Potential Contributors to Common Mode Error in Chuandian Region of China

2020 ◽  
Vol 12 (5) ◽  
pp. 751
Author(s):  
Weijie Tan ◽  
Junping Chen ◽  
Danan Dong ◽  
Weijing Qu ◽  
Xueqing Xu
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Principal Component ◽  
Mass Loading ◽  
Common Mode ◽  
Surface Mass ◽  
Common Mode Error ◽  
Comparison Results ◽  
Wide Range ◽  
Gps Time Series

Common mode error (CME) in Chuandian region of China is derived from 6-year continuous GPS time series and is identified by principal component analysis (PCA) method. It is revealed that the temporal behavior of the CME is not purely random, and contains unmodeled signals such as nonseasonal mass loadings. Its spatial distribution is quite uniform for all GPS sites in the region, and the first principal component, uniformly distributed in the region, has a spatial response of more than 70%. To further explore the potential contributors of CME, daily atmospheric mass loading and soil moisture mass loading effects are evaluated. Our results show that ~15% of CME can be explained by these daily surface mass loadings. The power spectral analysis is used to assess the CME. After removing atmospheric and soil moisture loadings from the CME, the power of the CME reduces in a wide range of frequencies. We also investigate the contribution of CME in GPS filtered residuals time series and it shows the Root Mean Squares (RMSs) of GPS time series are reduced by applying of the mass loading corrections in CME. These comparison results demonstrate that daily atmosphere pressure and the soil moisture mass loadings are a part of contributors to the CME in Chuandian region of China.

The 2018-2019 European drought sets a new benchmark over 250 years

2020 ◽  
Author(s):  
Oldrich Rakovec ◽  
Vittal Hari ◽  
Yannis Markonis ◽  
Luis Samaniego ◽  
Martin Hanel ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Clustering Algorithm ◽  
Hydrologic Model ◽  
Drought Event ◽  
Moisture Index ◽  
Economic Sectors ◽  
Wide Range ◽  
Hydroclimate Variability ◽  
Spatio Temporal ◽  
D 12

<p>The 21st-century droughts in Europe are regarded as exceptionally severe<br>and negatively affecting a wide range of socio-economic sectors due to<br>increases in temperature together with a lack of precipitation during<br>the spring/summer months [1]. In this study, we synthesize a space-time<br>evolution of soil moisture droughts in the period of 1766-2019<br>to better understand the evolution of large-domain multi-year droughts<br>reflecting the long-term historical changes in hydroclimate variability across Europe.</p><p>Following steps are taken to quantify the prolonged (multi-year) soil<br>moisture droughts: (1) simulate soil moisture (SM) with<br>the mesoscale Hydrologic Model (mHM, [2]) forced using several bias-corrected<br>meteorological merged products [3-5]; (2) estimate<br>quantile-based soil moisture index (SMI) based on a 254-year long<br>monthly dataset, which is estimated with a kernel density approach [6];<br>(3) perform a spatio-temporal clustering algorithm to track droughts<br>through space and time along their evolution, for a given threshold of<br>SMI<0.2 [6]; (4) estimate drought statistics such as areal extent,<br>duration, intensity for all identified soil moisture drought events. </p><p>The results from the period 1766-2019 show that total drought intensity<br>over Europe has an increasing trend, while the average<br>drought area remains unchanged.  In terms of total drought magnitude,<br>the ongoing recent 2018-2019 drought is ranked as the most extreme,<br>followed by 1920-1922, 1947-1948, 1857-1860, and 1988-1991<br>events. All these exceptional summer droughts were initiated in spring<br>primarily as a result of compounding effects of low precipitation and<br>high temperatures leading to extreme soil water<br>deficits. The 2018-2019 event exhibits average drought area covering<br>50% of the study domain, which is same as in 1947-1948. Our analysis<br>suggests that the 2018-2019 event is a new European drought benchmark,<br>replacing the well-documented 2003 drought event.</p><p>References:</p><p>[1] https://doi.org/10.1038/s41598-018-27464-4<br>[2] https://www.ufz.de/mhm<br>[3] https://doi.org/10.1007/s00382-007-0257-6<br>[4] https://doi.org/10.1002/joc.3711<br>[5] https://doi.org/10.1029/2009JD011799<br>[6] https://doi.org/10.1175/JHM-D-12-075.1</p>

scholarly journals Potential of Pan-European Seasonal Hydrometeorological Drought Forecasts Obtained from a Multihazard Early Warning System

2020 ◽  
Vol 101 (4) ◽  
pp. E368-E393 ◽  
Author(s):  
Samuel Jonson Sutanto ◽  
Henny A. J. Van Lanen ◽  
Fredrik Wetterhall ◽  
Xavier Llort
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Water Cycle ◽  
Warning System ◽  
Meteorological Drought ◽  
Early Warning Systems ◽  
Drought Indices ◽  
Drought Forecasting ◽  
Wide Range ◽  
Drought Early Warning

Abstract Drought early warning systems (DEWS) have been developed in several countries in response to high socioeconomic losses caused by droughts. In Europe, the European Drought Observatory (EDO) monitors the ongoing drought and forecasts soil moisture anomalies up to 7 days ahead and meteorological drought up to 3 months ahead. However, end users managing water resources often require hydrological drought warning several months in advance. To answer this challenge, a seasonal pan-European DEWS has been developed and has been running in a preoperational mode since mid-2018 under the EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) project. The ANYWHERE DEWS (AD-EWS) is different than other operational DEWS in the sense that the AD-EWS provides a wide range of seasonal hydrometeorological drought forecasting products in addition to meteorological drought, that is, a broad suite of drought indices that covers all water cycle components (drought in precipitation, soil moisture, runoff, discharge, and groundwater). The ability of the AD-EWS to provide seasonal drought predictions in high spatial resolution (5 km × 5 km) and its diverse products mark the AD-EWS as a preoperational drought forecasting system that can serve a broad range of different users’ needs in Europe. This paper introduces the AD-EWS and shows some examples of different drought forecasting products, the drought forecast score, and some examples of a user-driven assessment of forecast trust levels.

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