Journal Paper Digests

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Journal Paper Digests 2022 #9

  • Fuzzy map comparisons enable objective hydro-morphodynamic model validation
  • Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield
  • Soil moisture as an essential component for delineating and forecasting agricultural rather than meteorological drought
  • Rapid intensification of the emerging southwestern North American megadrought in 2020-2021

Rapid intensification of the emerging southwestern North American megadrought in 2020-2021

Southwestern North America has been experiencing lower than average precipitation and higher temperatures since 2000. This emerging megadrought, spanning 2000-2021, has been the driest 22-year period since the year 800 and 19% of the drought severity in 2021 can be attributed to climate change.

A previous reconstruction back to 800 ce indicated that the 2000-2018 soil moisture deficit in southwestern North America was exceeded during one megadrought in the late-1500s. Here, we show that after exceptional drought severity in 2021, similar to 19% of which is attributable to anthropogenic climate trends, 2000-2021 was the driest 22-yr period since at least 800. This drought will very likely persist through 2022, matching the duration of the late-1500s megadrought.

Soil moisture as an essential component for delineating and forecasting agricultural rather than meteorological drought

Drought is a recurring, complex, and extreme climatic phenomenon characterized by subnormal precipitation for months to years triggering negative impacts on agriculture, energy, tourism, recreation, and transportation sectors. Agricultural drought assessment is based on a deficit of soil moisture (SM) during the plant-growing season, whereas meteorological drought corresponds to subnormal precipitation over months to years. However, satellite-derived agricultural and meteorological drought indices (including those comprising root-zone SM) have not been comprehensively compared to evaluate their ability for drought delineation and particularly forecasting across climate regimes, land cover and soil types, and irrigation management (irrigated vs. rainfed) in the contiguous USA (CONUS). Here, we did so from 2015 to 2019 within the CONUS. In most regions except the US Midwest and Southeast, SM-based indices (e.g., Palmer Z, SMAP, SWDI) delineated agricultural drought better than meteorological (e.g., SPI, SPEI) and hybrid (Comprehensive Drought Index, CDI) drought indices. In contrast, the SPI and SPEI showed strong correlation with the aridity index in most part of the CONUS except the Midwest. SM-based and hybrid indices also demonstrated skills for agricultural drought forecasting (represented by end-of-year cumulative GPP), predominantly in the early growing season and particularly in irrigated rather than rainfed croplands. These findings indicate the leading role of SM in controlling ecosystem dryness and confirm “drought memory”, possibly due to SM-memory in land-atmosphere coupling. Proper application of meteorological and agricultural drought indices and their contrasting spatial-temporal controls on plant growth and ecosystem dryness has the potential to improve our understanding of drought evolution and provide early drought forecasting across large regions with diverse climate regimes, land cover types, soil textural classes, and irrigation management.

Farming and Earth Observation: Sentinel-2 data to estimate within-field wheat grain yield

Wheat grain yield (GY) is a crop feature of central importance affecting agricultural, environmental, and socioeconomic sustainability worldwide. Hence, the estimation of within-field variability of GY is pivotal for the agricultural management, especially in the current global change context. In this sense, Earth Observation Systems (EOS) are key technologies that use satellite data to monitor crop yield, which can guide the application of precision farming. Yet, novel research is required to improve the multiplatform integration of data, including data processing, and the application of this discipline in agricultural management. This article provides a novel methodological analysis and assessment of its applications in precision farming. It presents an integration of wheat GY, Global Positioning Systems (GPS), combine harvester data, and EOS Sentinel-2 multispectral bands. Moreover, it compares several indices and machine learning (ML) approaches to map within-field wheat GY. It also analyses the importance of multi-date remote sensing imagery and explores its potential applications in precision agriculture. The study was conducted in Spain, a major European wheat producer. Within-field GY data was obtained from a GPS combine harvester machine for 8 fields over three seasons (2017-2019) and consecutively processed to match Sentinel-2 10 m pixel size. Seven vegetation indices (NDVI, GNDVI, EVI, RVI, TGI, CVI and NGRDI) as well as the biophysical parameter LAI (leaf area index) retrieved with radiative transfer models (RTM) were calculated from Sentinel-2 bands. Sentinel-2 10 m resolution bands alone were also used as variables. Random forest, support vector machine and boosted regressions were used as modelling approaches, and multilinear regression was calculated as baseline. Different combinations of dates of measurement were tested to find the most suitable model feeding data. LAI retrieved from RTM had a slightly improved performance in estimating within-field GY in comparison with vegetation indices or Sentinel-2 bands alone. At validation, the use of multi-date Sentinel-2 data was found to be the most suitable in comparison with single date images. Thus, the model developed with random forest regression (e.g. R-2 = 0.89, and RSME = 0.74 t/ha when using LAI) outperformed support vector machine (R-2 = 0.84 and RSME = 0.92 t/ha), boosting regression (R-2 = 0.85 and RSME = 0.88 t/ha) and multilinear regression (R-2 = 0.69 and RSME = 1.29 t/ha). However, single date images at specific phenological stages (e.g. R-2 = 0.84, and RSME = 0.88 t/ha using random forest at stem elongation) also posed relatively high R-2 and low RMSE, with potential for precision farming management before harvest.

Fuzzy map comparisons enable objective hydro-morphodynamic model validation

Numerical modeling represents a state-of-the-art technique to simulate hydro-morphodynamic processes in river ecosystems. Numerical models are often validated based on observed topographic change in the form of pixel information on net erosion or deposition over a simulation period. When model validation is performed by a pixel-by-pixel comparison of exactly superimposed simulated and observed pixels, zero or negative correlation coefficients are often calculated, suggesting poor model performance. Thus, a pixel-by-pixel approach penalizes quantitative simulation errors, even if a model conceptually works well. To distinguish between reasonably well-performing and non-representative models, this study introduces and tests fuzzy map comparison methods. First, we use a fuzzy numerical map comparison to compensate for spatial offset errors in correlation analyses. Second, we add a level of fuzziness with a fuzzy kappa map comparison to additionally address quantitative inaccuracy in modeled topographic change by categorizing data. Sample datasets from a physical lab model and datasets from a 6.9 km long gravel-cobble bed river reach enable the verification of the relevance of fuzzy map comparison methods. The results indicate that a fuzzy numerical map comparison is a viable technique to compensate for model errors stemming from spatial offset. In addition, fuzzy kappa map comparisons are suitable for objectively expressing subjectively perceived correlation between two maps, provided that a small number of categories is used. The methods tested and the resulting spatially explicit comparison maps represent a significant opportunity to improve the evaluation and potential calibration of numerical models of river ecosystems in the future.

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