Journal Paper Digests

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Journal Paper Digests 2018 #23

  • Comparing optimal and empirical stomatal conductance models for application in Earth system models
  • SMAP soil moisture improves global evapotranspiration
  • Prediction of drought-induced reduction of agricultural productivity in Chile from MODIS, rainfall estimates, and climate oscillation indices
  • Susquehanna Shale Hills Critical Zone Observatory: Shale Hills in the Context of Shaver’s Creek Watershed
  • Cosmic Ray Neutron Sensing for Simultaneous Soil Water Content and Business Quantification in Drought conditions
  • Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes
  • Management of the major chemical soil constraints affecting yields in the grain growing region of Queensland and New South Wales, Australia - a review
  • Spatial variation in soil organic carbon and nitrogen at two field sites under crop and pasture rotations in southern New South Wales, Australia
  • AgrHyS: An Observatory of Response Times in Agro-Hydro Systems
  • OZCAR: The French Network of Critical Zone Observatories

Comparing optimal and empirical stomatal conductance models for application in Earth system models

Authors: Franks, PJ; Bonan, GB; Berry, JA; Lombardozzi, DL; Holbrook, NM; Herold, N; Oleson, KW

Source: GLOBAL CHANGE BIOLOGY, 24 (12):5708-5723; DEC 2018

Abstract: Earth system models (ESMs) rely on the calculation of canopy conductance in land surface models (LSMs) to quantify the partitioning of land surface energy, water, and CO2 fluxes. This is achieved by scaling stomatal conductance, g(w), determined from physiological models developed for leaves. Traditionally, models for g(w) have been semi-empirical, combining physiological functions with empirically determined calibration constants. More recently, optimization theory has been applied to model g(w) in LSMs under the premise that it has a stronger grounding in physiological theory and might ultimately lead to improved predictive accuracy. However, this premise has not been thoroughly tested. Using original field data from contrasting forest systems, we compare a widely used empirical type and a more recently developed optimization-type g(w) model, termed BB and MED, respectively. Overall, we find no difference between the two models when used to simulate g(w) from photosynthesis data, or leaf gas exchange from a coupled photosynthesis-conductance model, or gross primary productivity and evapotranspiration for a FLUXNET tower site with the CLM5 community LSM. Field measurements reveal that the key fitted parameters for BB and MED, g(1B) and g(1M,) exhibit strong species specificity in magnitude and sensitivity to CO2, and CLM5 simulations reveal that failure to include this sensitivity can result in significant overestimates of evapotranspiration for high-CO2 scenarios. Further, we show that g(1B) and g(1M) can be determined from mean c(i)/c(a) (ratio of leaf intercellular to ambient CO2 concentration). Applying this relationship with c(i)/c(a) values derived from a leaf delta C-13 database, we obtain a global distribution of g(1B) and g(1M), and these values correlate significantly with mean annual precipitation. This provides a new methodology for global parameterization of the BB and MED models in LSMs, tied directly to leaf physiology but unconstrained by spatial boundaries separating designated biomes or plant functional types.

SMAP soil moisture improves global evapotranspiration

Authors: Purdy, AJ; Fisher, JB; Goulden, ML; Colliander, A; Halverson, G; Tu, K; Farniglietti, JS

Source: REMOTE SENSING OF ENVIRONMENT, 219 1-14; DEC 15 2018

Abstract: Accurate estimation of global evapotranspiration (ET) is essential to understand water cycle and land-atmosphere feedbacks in the Earth system. Satellite-driven ET models provide global estimates, but many of the ET algorithms have been designed independently of soil moisture observations. As water for ET is sourced from the soil, incorporating soil moisture into global remote sensing algorithms of ET should, in theory, improve performance, especially in water-limited regions. This paper presents an update to the widely-used Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) ET algorithm to incorporate spatially explicit daily surface soil moisture control on soil evaporation and canopy transpiration. The updated algorithm is evaluated using 14 AmeriFlux eddy covariance towers co-located with COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture observations. The new PT-JPL(SM) model shows reduced errors and increased explanation of variance, with the greatest improvements in water-limited regions. Soil moisture incorporation into soil evaporation improves ET estimates by reducing bias and RMSE by 29.9% and 22.7% respectively, while soil moisture incorporation into transpiration improves ET estimates by reducing bias by 30.2%, RMSE by 16.9%. We apply the algorithm globally using soil moisture observations from the Soil Moisture Active Passive Mission (SMAP). These new global estimates of ET show reduced error at finer spatial resolutions and provide a rich dataset to evaluate land surface and climate models, vegetation response to changes in water availability and environmental conditions, and anthropogenic perturbations to the water cycle.

Prediction of drought-induced reduction of agricultural productivity in Chile from MODIS, rainfall estimates, and climate oscillation indices

Authors: Zambrano, F; Vrieling, A; Nelson, A; Meroni, M; Tadesse, T

Source: REMOTE SENSING OF ENVIRONMENT, 219 15-30; DEC 15 2018

Abstract: Global food security is negatively affected by drought. Climate projections show that drought frequency and intensity may increase in different parts of the globe. These increases are particularly hazardous for developing countries. Early season forecasts on drought occurrence and severity could help to better mitigate the negative consequences of drought. The objective of this study was to assess if interannual variability in agricultural productivity in Chile can be accurately predicted from freely-available, near real-time data sources. As the response variable, we used the standard score of seasonal cumulative NDVI (zcNDVI), based on 2000-2017 data from Moderate Resolution Imaging Spectroradiometer (MODIS), as a proxy for anomalies of seasonal primary productivity. The predictions were performed with forecast lead times from one- to six-month before the end of the growing season, which varied between census units in Chile. Predictor variables included the zcNDVI obtained by cumulating NDVI from season start up to prediction time; standardised precipitation indices derived from satellite rainfall estimates, for time-scales of 1, 3, 6, 12 and 24 months; the Pacific Decadal Oscillation and the Multivariate ENSO oscillation indices; the length of the growing season, and latitude and longitude. For each of the 758 census units considered, the time series of the response and the predictor variables were averaged for agricultural areas resulting in a 17-season time series per unit for each variable. We used two prediction approaches: (i) optimal linear regression (OLR) whereby for each census unit the single predictor was selected that best explained the interannual zcNDVI variability, and (ii) a multi-layer feedforward neural network architecture, often called deep learning (DL), where all predictors for all units were combined in a single spatio-temporal model. Both approaches were evaluated with a leave-one-year-out cross-validation procedure. Both methods showed good prediction accuracies for small lead times and similar values for all lead times. The mean R-cv(2) values for OLR were 0.95, 0.83, 0.68, 0.56, 0.46 and 0.37, against 0.96, 0.84, 0.65, 0.54, 0.46 and 0.38 for DL, for one, two, three, four, five, and six months lead time, respectively. Given the wide range of climates and vegetation types covered within the study area, we expect that the presented models can contribute to an improved early warning system for agricultural drought in different geographical settings around the globe.

Susquehanna Shale Hills Critical Zone Observatory: Shale Hills in the Context of Shaver’s Creek Watershed

Authors: Brantley, SL; White, T; West, N; Williams, JZ; Forsythe, B; Shapich, D; Kaye, J; Lin, H; Shi, YN; Kaye, M; Herndon, E; Davis, KJ; He, Y; Eissenstat, D; Weitzman, J; DiBiase, R; Li, L; Reed, W; Brubaker, K; Gu, X

Source: VADOSE ZONE JOURNAL, 17 (1):80092-80092; NOV 15 2018

Abstract: The Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) was established to investigate the form, function, and dynamics of the critical zone developed on sedimentary rocks in the Appalachian Mountains in central Pennsylvania. When first established, the SSHCZO encompassed only the Shale Hills catchment, a 0.08-km(2) subcatchment within Shaver’s Creek watershed. The SSHCZO has now grown to include 120 km(2) of the Shaver’s Creek watershed. With that growth, the science team designed a strategy to measure a parsimonious set of data to characterize the critical zone in such a large watershed. This parsimonious design includes three targeted subcatchments (including the original Shale Hills), observations along the main stem of Shaver’s Creek, and broad topographic and geophysical observations. Here we describe the goals, the implementation of measurements, and the major findings of the SSHCZO by emphasizing measurements of the main stem of Shaver’s Creek as well as the original Shale Hills subcatchment.

Cosmic Ray Neutron Sensing for Simultaneous Soil Water Content and Business Quantification in Drought conditions

Authors: Jakobi, J; Huisman, JA; Vereecken, H; Diekkruger, B; Bogena, HR

Source: WATER RESOURCES RESEARCH, 54 (10):7383-7402; OCT 2018

Abstract: Understanding the feedback mechanisms between soil water content (SWC) and biomass production is important for sustainable resources management. Here we present a new method enabling simultaneous noninvasive measurements of SWC and biomass dynamics based on cosmic ray neutron sensing (CRNS). Recently, it was suggested that the neutron ratio (N-r) between thermal neutron (TN) and fast neutron (FN) intensity contains information on other hydrogen pools like vegetation, canopy interception, and snow. The aim of this study is to evaluate the accuracy of simultaneous measurements of SWC and biomass dynamics during agricultural drought conditions using CRNS probes. To this end, we instrumented an arable field cropped with sugar beet with CRNS probes and a wireless in situ SWC sensor network. Belowground and aboveground biomass were sampled in monthly intervals. We found a linear relationship between N-r and the aboveground biomass that allowed to continuously quantify the dry aboveground biomass development throughout the growing season with a root-mean-square error from 0.14 to 0.22 kg/m(2). This information was used together with measured belowground biomass to correct for the effect of biomass on SWC determination with CRNS probes, which increased the accuracy of the SWC estimates considerably as indicated by the decrease of the root-mean-square error from 0.046 to 0.013 cm(3)/cm(3). We anticipate that future research on the N-r can further improve the accuracy of SWC and biomass estimates and extend the application of CRNS to include canopy interception, ponding water, and snow water equivalent estimation for both stationary and roving CRNS systems.

Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes

Authors: Papalexiou, SM; Markonis, Y; Lombardo, F; AghaKouchak, A; Foufoula-Georgiou, E

Source: WATER RESOURCES RESEARCH, 54 (10):7435-7458; OCT 2018

Abstract: Hydroclimatic variables such as precipitation and temperature are often measured or simulated by climate models at coarser spatiotemporal scales than those needed for operational purposes. This has motivated more than half a century of research in developing disaggregation methods that break down coarse-scale time series into finer scales, with two primary objectives: (a) reproducing the statistical properties of the fine-scale process and (b) preserving the original coarse-scale data. Existing methods either preserve a limited number of statistical moments at the fine scale, which is often insufficient and can lead to an unrepresentative approximation of the actual marginal distribution, or are based on a limited number of a priori distributional assumptions, for example, lognormal. Additionally, they are not able to account for potential nonstationarity in the underlying fine-scale process. Here we introduce a novel disaggregation method, named Disaggregation Preserving Marginals and Correlations (DiPMaC), that is able to disaggregate a coarse-scale time series to any finer scale, while reproducing the probability distribution and the linear correlation structure of the fine-scale process. DiPMaC is also generalized for arbitrary nonstationary scenarios to reproduce time varying marginals. Additionally, we introduce a computationally efficient algorithm, based on Bernoulli trials, to optimize the disaggregation procedure and guarantee preservation of the coarse-scale values. We focus on temporal disaggregation and demonstrate the method by disaggregating monthly precipitation to hourly, and time series with trends (e.g., climate model projections), while we show its potential to disaggregate based on general nonstationary scenarios. The example applications demonstrate the performance and robustness of DiPMaC.

Management of the major chemical soil constraints affecting yields in the grain growing region of Queensland and New South Wales, Australia - a review

Authors: Page, KL; Dalal, RC; Wehr, JB; Dang, YP; Kopittke, PM; Kirchhof, G; Fujinuma, R; Menzies, NW

Source: SOIL RESEARCH, 56 (8):765-779; 2018

Abstract: In the grain growing region of Queensland and New South Wales, Australia, crop production occurs predominantly under semiarid, rainfed conditions. Vertosols dominate the soils used and many are prone to structural problems. In this region, providing that crop nutrition is adequate, optimising yield is largely dependent on maximising the infiltration, storage and plant use of soil water. Soil constraints such as sodicity, salinity, acidity, subsoil compaction and surface sealing can limit these processes, leading to high yield losses. This review examines management options to treat these constraints, focusing on management where multiple constraints exist, and where these occur in the subsoil. The main strategies reviewed include (a) use of gypsum to treat sodicity and lime to treat acidity, which can lead to yield increases of >100% in some circumstances, (b) cultivation or deep ripping to break up compacted sodic layers and surface seals, (c) incorporating soil organic matter to improve conditions for plant growth and (d) selecting species, cultivars and management practices most appropriate for constrained sites. Future research must be directed to improving the profitability of ameliorant use for sodicity by increasing our understanding of how to identify soils responsive to ameliorants, and which combination of ameliorants will be cost effective when sodicity occurs in combination with other constraints. In addition, research needs to target ways to economically apply ameliorants in subsoil environments, and better identify which crop species or cultivars are productive on constrained sites, particularly those with multiple constraints.

Spatial variation in soil organic carbon and nitrogen at two field sites under crop and pasture rotations in southern New South Wales, Australia

Authors: Conyers, M; Orchard, B; Orgill, S; Oates, A; Poile, G; Hayes, R; Hawkins, P; Xu, BB; Jia, Y; van der Rijt, V; Li, GD

Source: SOIL RESEARCH, 56 (8):780-792; 2018

Abstract: Estimating the likely variance in soil organic carbon (OC) at the scale of farm fields or smaller monitoring areas is necessary for developing sampling protocols that allow temporal change to be detected. Given the relatively low anticipated soil OC sequestration rates (<0.5 Mg/ha.0.30 m/year) for dryland agriculture it is important that sampling strategies are designed to reduce any cumulative errors associated with measuring soil OC. The first purpose of this study was to evaluate the spatial variation in soil OC and nitrogen (N), in soil layers to 1.50 m depth at two monitoring sites (Wagga Wagga and Yerong Creek, 0.5 ha each) in southern New South Wales, Australia, where crop and pasture rotations are practiced. Four variogram models were tested (linear, spherical, Gaussian and exponential); however, no single model dominated across sites or depths for OC or N. At both sites, the range was smallest in surface soil, and on a scale suggesting that sowing rows (stubble) may dominate the pattern of spatial dependence, whereas the longer ranges appeared to be associated with horizon boundaries. The second purpose of the study was to obtain an estimate of the population mean with 1%, 5% and 10% levels of precision using the calculated variance. The number of soil cores required for a 1% precision in estimation of the mean soil OC or N was impractical at most depths (>500 per ha). About 30 soil cores per composite sample to 1.50 m depth, each core being at least 10 m apart, would ensure at least an average of 10% precision in the estimation of the mean soil OC at these two sites, which represent the agriculture of the region.

AgrHyS: An Observatory of Response Times in Agro-Hydro Systems

Authors: Fovet, O; Ruiz, L; Gruau, G; Akkal, N; Aquilina, L; Busnot, S; Dupas, R; Durand, P; Faucheux, M; Fauvel, Y; Flechard, C; Gilliet, N; Grimaldi, C; Hamon, Y; Jaffrezic, A; Jeanneau, L; Labasque, T; Le Henaff, G; Merot, P; Molenat, J; Petitjean, P; Pierson-Wickmann, AC; Squividant, H; Viaud, V; Walter, C; Gascuel-Odoux, C

Source: VADOSE ZONE JOURNAL, 17 (1):80066-80066; NOV 29 2018

Abstract: The AgrHyS is a long-term agro-hydrological observatory dedicated to studying the processes controlling hydro-chemical fluxes in headwater catchments in response to the effects of agricultural. AgrHyS is composed of instrumented catchments located in western France in a temperate oceanic climate that are characterized by shallow groundwater (<8 m deep) over crystalline bedrocks (granite or schist) and is dominated by intensive agriculture with farming. AgrHyS provides long-term observations starting in 1990 and supports highly interdisciplinary studies that provide novel contributions to environmental sciences, including hydrology, geochemistry, agricultural and soil sciences, hydrogeology, bioclimatology, and ecology. Here we describe the observatory sites, observation strategy, data management policy, and data access. The objective is to show how AgrHyS has contributed to research in hydrological and environmental sciences through a review of major insights of the research. This analysis highlights the role of AgrHyS in linking, validating, and enriching successive and complementary projects conducted over the last 25 yr. The second objective is to invite new collaborations with a large scientific community for future research.

OZCAR: The French Network of Critical Zone Observatories

Authors: Gaillardet, J; Braud, I; Hankard, F; Anquetin, S; Bour, O; Dorfliger, N; de Dreuzy, JR; Galle, S; Galy, C; Gogo, S; Gourcy, L; Habets, F; Laggoun, F; Longuevergne, L; Le Borgne, T; Naaim-Bouvet, F; Nord, G; Simonneaux, V; Six, D; Tallec, T; Valentin, C; Abril, G; Allemand, P; Arenes, A; Arfib, B; Arnaud, L; Arnaud, N; Arnaud, P; Audry, S; Comte, VB; Batiot, C; Battais, A; Bellot, H; Bernard, E; Bertrand, C; Bessiere, H; Binet, S; Bodin, J; Bodin, X; Boithias, L; Bouchez, J; Boudevillain, B; Moussa, IB; Branger, F; Braun, JJ; Brunet, P; Caceres, B; Calmels, D; Cappelaere, B; Celle-Jeanton, H; Chabaux, F; Chalikakis, K; Champollion, C; Copard, Y; Cotel, C; Davy, P; Deline, P; Delrieu, G; Demarty, J; Dessert, C; Dumont, M; Emblanch, C; Ezzahar, J; Esteves, M; Favier, V; Faucheux, M; Filizola, N; Flammarion, P; Floury, P; Fovet, O; Fournier, M; Francez, AJ; Gandois, L; Gascuel, C; Gayer, E; Genthon, C; Gerard, MF; Gilbert, D; Gouttevin, I; Grippa, M; Gruau, G; Jardani, A; Jeanneau, L; Join, JL; Jourde, H; Karbou, F; Labat, D; Lagadeuc, Y; Lajeunesse, E; Lastennet, R; Lavado, W; Lawin, E; Lebel, T; Le Bouteiller, C; Legout, C; Lejeune, Y; Le Meur, E; Le Moigne, N; Lions, J; Lucas, A; Malet, JP; Marais-Sicre, C; Marechal, JC; Marlin, C; Martin, P; Martins, J; Martinez, JM; Massei, N; Mauclerc, A; Mazzilli, N; Molenat, J; Moreira-Turcq, P; Mougin, E; Morin, S; Ngoupayou, JN; Panthou, G; Peugeot, C; Picard, G; Pierret, MC; Porel, G; Probst, A; Probst, JL; Rabatel, A; Raclot, D; Ravanel, L; Rejiba, F; Rene, P; Ribolzi, O; Riotte, J; Riviere, A; Robain, H; Ruiz, L; Sanchez-Perez, JM; Santini, W; Sauvage, S; Schoeneich, P; Seidel, JL; Sekhar, M; Sengtaheuanghoung, O; Silvera, N; Steinmann, M; Soruco, A; Tallec, G; Thibert, E; Lao, DV; Vincent, C; Viville, D; Wagnon, P; Zitouna, R

Source: VADOSE ZONE JOURNAL, 17 (1):80067-80067; NOV 29 2018

Abstract: The French critical zone initiative, called OZCAR (Observatoires de la Zone Critique-Application et Recherche or Critical Zone Observatories-Application and Research) is a National Research Infrastructure (RI). OZCAR-RI is a network of instrumented sites, bringing together 21 pre-existing research observatories monitoring different compartments of the zone situated between “the rock and the sky,” the Earth’s skin or critical zone (CZ), over the long term. These observatories are regionally based and have specific initial scientific questions, monitoring strategies, databases, and modeling activities. The diversity of OZCAR-RI observatories and sites is well representative of the heterogeneity of the CZ and of the scientific communities studying it. Despite this diversity, all OZCAR-RI sites share a main overarching mandate, which is to monitor, understand, and predict (“earthcast”) the fluxes of water and matter of the Earth’s near surface and how they will change in response to the “new climatic regime.” The vision for OZCAR strategic development aims at designing an open infrastructure, building a national CZ community able to share a systemic representation of the CZ, and educating a new generation of scientists more apt to tackle the wicked problem of the Anthropocene. OZCAR articulates around: (i) a set of common scientific questions and cross-cutting scientific activities using the wealth of OZCAR-RI observatories, (ii) an ambitious instrumental development program, and (iii) a better interaction between data and models to integrate the different time and spatial scales. Internationally, OZCAR-RI aims at strengthening the CZ community by providing a model of organization for pre-existing observatories and by offering CZ instrumented sites. OZCAR is one of two French mirrors of the European Strategy Forum on Research Infrastructure (eLTER-ESFRI) project.

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