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Cool farm tool water
(2018)
The agricultural sector accounts for 70% of all water consumption and poses great pressure on ground water resources. Therefore, evaluating agricultural water consumption is highly important as it allows supply chain actors to identify practices which are associated with unsustainable water use, which risk depleting current water resources and impacting future production. However, these assessments are often not feasible for crop producers as data, models and experiments are required in order to conduct them. This work introduces a new on-line agricultural water use assessment tool that provides the water footprint and irrigation requirements at field scale based on an enhanced FAO56 approach combined with a global climate, crop and soil databases. This has been included in the Cool Farm Tool - an online tool which already provides metrics for greenhouse gas emissions and biodiversity impacts and therefore allows for a more holistic assessment of environmental sustainability in farming and agricultural supply chains. The model is tested against field scale and state level water footprint data providing good results. The tool provides a practical, reliable way to assess agricultural water use, and offers a means to engage growers and stakeholders in identifying efficient water management practices. (C) 2018 The Authors. Published by Elsevier Ltd.
Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro-sedimentological model WASA-SED for the Mediterranean-mountainous Isabena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases.
Cosmic-ray neutron sensing (CRNS) is a promising proximal soil sensing technique to estimate soil moisture at intermediate scale and high temporal resolution. However, the signal shows complex and non-unique response to all hydrogen pools near the land surface, providing some challenges for soil moisture estimation in practical applications. Aims of the study were 1) to assess the uncertainty of CRNS as a stand-alone approach to estimate volumetric soil moisture in cropped field 2) to identify the causes of this uncertainty 3) and possible improvements. Two experimental sites in Germany were equipped with a CRNS probe and point-scale soil moisture network. Additional monitoring activities were conducted during the crop growing season to characterize the soil-plant systems. This data is used to identify and quantify the different sources of uncertainty (factors). An uncertainty analysis, based on Monte Carlo approach, is applied to propagate these uncertainties to CRNS soil moisture estimations. In addition, a sensitivity analysis based on the Sobol’ method is performed to identify the most important factors explaining this uncertainty. Results show that CRNS soil moisture compares well to the soil moisture network when these point-scale values are weighted to account for the spatial sensitivity of the signal and other sources of hydrogen (lattice water and organic carbon) are added to the water content. However, the performance decreases when CRNS is considered as a stand-alone method to retrieve the actual (non-weighted) volumetric soil moisture. The support volume (penetration depth and radius) shows also a considerable uncertainty, especially in relatively dry soil moisture conditions. Four of the seven factors analyzed (the vertical soil moisture profile, bulk density, incoming neutron correction and the calibrated parameter N0) were found to play an important role. Among the possible improvements identified, a simple correction factor based on vertical point-scale soil moisture profiles shows to be a promising approach to account for the sensitivity of the CRNS signal to the upper soil layers.