Chemical Versus Mechanical Denudation in Meta-Clastic and Carbonate Bedrock Catchments on Crete, Greece, and Mechanisms for Steep and High Carbonate Topography
. Journal of Geophysical Research: Earth Surface 2019
, 124. Publisher's VersionAbstract
Abstract On Crete—as is common elsewhere in the Mediterranean—carbonate massifs form high mountain ranges whereas topography is lower in areas with meta-clastic rocks. This observation suggests that differences in denudational processes between carbonate-rich rocks and quartzofeldspathic units impart a fundamental control on landscape evolution. Here we present new cosmogenic basin-average denudation rate measurements from both 10Be and 36Cl in meta-clastic and carbonate bedrock catchments, respectively, to assess relationships between denudation rates, processes, and topographic form. We compare total denudation rates to dissolution rates calculated from 49 new and previously published water samples. Basin-average denudation rates of meta-clastic and carbonate catchments are similar, with mean values of 0.10 mm/a and 0.13 mm/a, respectively. The contribution of dissolution to total denudation rate was <10% in the one measured meta-clastic catchment, and 40% for carbonate catchments ( 0.05 mm/a), suggesting the dominance of physical over chemical weathering at the catchment scale in both rock types. Water mass-balance calculations for three carbonate catchments suggests 40–90% of surface runoff is lost to groundwater. To explore the impact of dissolution and infiltration to groundwater on relief, we develop a numerical model for carbonate denudation. We find that dissolution modifies the river profile channel steepness, and infiltration changes the fluvial response time to external forcing. Furthermore, we show that infiltration of surface runoff to groundwater in karst regions is an efficient way to steepen topography and generate the dramatic relief in carbonates observed throughout Crete and the Mediterranean.
Early prediction of wheat grain yield production from root-zone soil water content at heading using Crop RS-Met
. Field Crops Research 2019
, 11 - 23. Publisher's VersionAbstract
Wheat production in drylands is determined greatly by the available water at the critical growth stages. In dry years, farmers usually face the dilemma of whether to harvest at an early stage for hay or silage, with reduced profit, or leave the crop for grain production with the risk of a major economic loss. Thus, an early prediction of potential wheat grain yield production is essential for agricultural decision making, particularly in water-limited areas. Here, we test whether using a proximal-based biophysical model of actual evapotranspiration (water use) and root-zone soil water content (SWC) – Crop RS-Met – may assist in providing early grain yield predictions in dryland wheat fields. Crop RS-Met was examined in eight experimental fields comprising a variety of spring wheat (Triticum aestivum L.) cultivars exposed to different treatments and amounts of water supply (185 mm - 450 mm). Crop RS-Met was first validated against SWC measurements at the root-zone profile. Then, modeled SWC at heading (SWCHeading) was regressed against end-of-season grain yields (GYEOS), which ranged from 1.30 tons ha−1 to 7.12 tons ha−1, for a total of 56 treatment blocks in 4 seasonal years (2014–2017). Results show that Crop RS-Met accurately reproduce seasonal changes in SWC with an average R2 of 0.89 ± 0.05 and RMSE and bias of 0.014 ± 0.004 m3 m−3 and -0.002 ± 0.004 m3 m−3, respectively. Modeled SWCHeading showed high and significant positive linear relationship with GYEOS (GYEOS[tons ha-1] = 0.080×SWCHeading[mm] - 5.387; R2 = 0.90; P < 0.001; N=56). Moreover, Crop RS-Met showed to be capable of accurately predicting GYEOS even in cases where water supply and grain yield had adverse relationships. Aggregating results to the field-scale level and classifying fields per water supply conditions resulted in an even stronger linear relationship (R2 = 0.94; P < 0.001; N=9). We conclude that Crop RS-Met may be used to predict GYEOS at heading in dryland fields for possible use by farmers in decision making at critical wheat growth stages.
Crop RS-Met: A biophysical evapotranspiration and root-zone soil water content model for crops based on proximal sensing and meteorological data
. Agricultural Water Management 2019
, 210 - 219. Publisher's VersionAbstract
Assessing crops water use is essential for agricultural water management and planning, particularly in water-limited regions. Here, we present a biophysical model to estimate crop actual evapotranspiration and root-zone soil water content using proximal sensing and meteorological data (Crop RS-Met). The model, which is based on the dual FAO56 formulation, uses a water deficit factor calculated from rainfall and atmospheric demand information to constrain actual evapotranspiration and soil water content in crops growing under dry conditions. We tested the Crop RS-Met model in a dryland experimental field comprising a variety of wheat (Triticum aestivum L. and T. durum) cultivars with diverse phenology. Crop RS-Met was shown to accurately capture seasonal changes in wheat water use during the growing season. The average R2 of modeled vs. observed soil water content for all cultivars (N = 11) was 0.92 ± 0.02 with average relative RMSE and bias of 9.29 ± 1.30% and 0.13 ± 0.03%, respectively. We found that changing the integration time period of the water deficit factor in Crop RS-Met affects the accuracy of the model implying that this factor has a vital role in modeling crop water use under dry conditions. Currently, Crop RS-Met has a simple representation of surface runoff and does not take into consideration heterogeneity in the soil profile. Thus, efforts to combine numerical models that simulate soil water dynamics with a Crop RS-Met model driven by high-resolution remote sensing data may be needed for a spatially continuous assessment of crop water use in fields with more complex edaphic characteristics.
Explicit wheat production model adjusted for semi-arid environments
. Field Crops Research 2019
, 93 - 104. Publisher's VersionAbstract
Current literature suggests that wheat production models are limited either to wide-scale or plot-based predictions ignoring pattern of habitat conditions and surficial hydrological processes. We present here a high-spatial resolution (50 m) non-calibrated GIS-based wheat production model for predictions of aboveground wheat biomass (AGB) and grain yield (GY). The model is an integration of three sub-models, each simulating elemental processes relevant for wheat growth dynamics in water-limited environments: (1) HYDRUS-1D, a finite element model that simulates one-dimensional movement of water in the soil profile; (2) a two-dimensional GIS-based surface runoff model; and (3) a one-dimensional process-driven mechanistic wheat growth model. By integrating the three sub-models, we aimed to achieve a more accurate spatially continuous water balance simulation with a better representation of root zone soil water content (SWC) impacts on plant development. High-resolution grid-based rainfall data from a meteorological radar system were used as input to HYDRUS-1D. Twenty-two commercial wheat fields in Israel were used to validate the model in two seasons (2010/11 and 2011/12). Results show that root zone SWC was accurately simulated by HYDRUS-1D in both seasons, particularly at the top 10-cm soil layer. Observed vs simulated AGB and GY were highly correlated with R2 = 0.93 and 0.72 (RMSE = 171 g m−2 and 70 g m−2) having low biases of -41 g m−2 (8%) and 52 g m−2 (10%), respectively. Model sensitivity test showed that HYDRUS-1D was mainly driven by spatial variability in the input soil characteristics while the integrated wheat production model was mostly affected by rainfall spatial variability indicating the importance of using accurate high-resolution rainfall data as model input. Using the integrated model, we predict decreases in AGB and GY of c. 10.5% and c. 12%, respectively, for 1 °C of warming and c. 7.7% and c. 7.3% for 5% reduction in rainfall amount in our study sites. The suggested model could be used by scientists to better understand the causes of spatial and temporal variability in wheat production and the consequences of future scenarios such as climate change.