A Parallel Computation Tool to Enable Dynamic Sensitivity and Model Performance Analysis of APEX: Evapotranspiration Modeling

Global sensitivity analysis can be used for assessing the relative importance of model parameters on model outputs. The sensitivity of parameters usually indicates a temporal variation due to variation in the environmental conditions (e.g., variation in weather or plant growth). In addition, the size of averaging window by which the outputs of a model are aggregated or averaged may impact parameter sensitivities. In this study, temporal variation of parameters sensitives, model performance, as well as the impact of the size of time‐averaging window on evapotranspiration (ET) prediction using the Agricultural Policy/Environmental eXtender (APEX) model are investigated. To achieve these goals, an open‐source package named PARAPEX was developed in R and used to perform dynamic sensitivity and model performance analysis of APEX using parallel computation. PARAPEX reduced the computation time from 5,939 to 379 s (using 20 and 1 computation nodes, respectively). The sensitivity analysis results indicated the parameters accounting for the reducing effect of plant cover on evaporation from the soil surface, the effect of soil on the plant root growth, and the effect of cycling and transformation dynamics of organic matter at the top soil layer as the top sensitive parameters based on the mean daily simulated ET and the Nash–Sutcliffe model performance measure. The dynamic performance analysis indicated poor ET predictions by APEX during the growing seasons. Editor's note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Transition Pathways to Sustainable Agricultural Water Management: A Review of Integrated Modeling Approaches

Agricultural water management (AWM) is an interdisciplinary concern, cutting across traditional domains such as agronomy, climatology, geology, economics, and sociology. Each of these disciplines has developed numerous process‐based and empirical models for AWM. However, models that simulate all major hydrologic, water quality, and crop growth processes in agricultural systems are still lacking. As computers become more powerful, more researchers are choosing to integrate existing models to account for these major processes rather than building new cross‐disciplinary models. Model integration carries the hope that, as in a real system, the sum of the model will be greater than the parts. However, models based upon simplified and unrealistic assumptions of physical or empirical processes can generate misleading results which are not useful for informing policy. In this article, we use literature and case studies from the High Plains Aquifer and Southeastern United States regions to elucidate the challenges and opportunities associated with integrated modeling for AWM and recommend conditions in which to use integrated models. Additionally, we examine the potential contributions of integrated modeling to AWM — the actual practice of conserving water while maximizing productivity. Editor's note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Assessment of biomass and lipid productivity and biodiesel quality of an indigenous microalga Chlorella sorokiniana MIC-G5

Generation of biodiesel from microalgae has been extensively investigated; however, its quality is often not suitable for use as fuel. Our investigation involved the evaluation of biodiesel quality using a native isolate Chlorella sorokiniana MIC-G5, as specified by American Society for Testing and Materials (ASTM), after transesterification of lipids with methanol, in the presence of sodium methoxide. Total quantity of lipids extracted from dry biomass, of approximately 410–450 mg g^?1 was characterized using FTIR and ¹H NMR. After transesterification, the total saturated and unsaturated fatty acid methyl esters (FAMEs) were 43% and 57%, respectively. The major FAMEs present in the biodiesel were methyl palmitate (C16:0), methyl oleate (C18:1), and methyl linoleate (C18:2), and the ¹H NMR spectra matched with criteria prescribed for high-quality biodiesel. The biodiesel exhibited a density of 0.873 g cm^–3, viscosity of 3.418 mm² s^?1, cetane number (CN) of 57.85, high heating value (HHV) of 40.25, iodine value of 71.823 g I₂ 100 g^?1, degree of unsaturation (DU) of 58%, and a cold filter plugging point (CFPP) of –5.22°C. Critical fuel parameters, including oxidation stability, CN, HHV, iodine value, flash point, cloud point, pour point, density, and viscosity were in accordance with the methyl ester composition and structural configuration. Hence, C. sorokiniana can be a promising feedstock for biodiesel generation.     

Evaluation of Satellite‐Derived Rainfall Data for Multiple Physio‐Climatic Regions in the Santiago River Basin,Mexico

Assessment of water resources requires reliable rainfall data, and rain gauge networks may not provide adequate spatial representation due to limited point measurements. The Tropical Rainfall Measuring Mission (TRMM) provides rainfall data at global scale, and has been used with good results. However, TRMM data are an indirect measurement of rainfall, and therefore must be validated for its proper use. In this work, a validation scheme was designed and implemented to compare the TRMM Version 7 (V7) monthly rainfall product at different time frames with data measured in two hydrologic subregions of the Santiago River Basin (SRB) in Mexico: Río Alto Santiago and Río Bajo Santiago (RBS). Additionally, three physio‐climatic regions provide an assessment of the interplay of topography, distance from coastal regions, and seasonal weather patterns on the correspondence between both datasets. The TRMM V7 rainfall product exhibited good agreement with the rain gauge data particularly for the RBS and for the whole SRB during wettest summer and autumn seasons. However, strong regional dependence was observed due to differences in climate and topography. Overall, in spite of some noted underestimations, the monthly TRMM V7 rainfall product was found to provide useful information that can be used to complement limited monitoring as is the case of RBS. An improved combined rainfall product could be generated and thus gaining the most benefits from both data sources.     

Impact of Length of Dataset on Streamflow Calibration Parameters and Performance of APEX Model

Due to resource constraints, long‐term monitoring data for calibration and validation of hydrologic and water quality models are rare. As a result, most models are calibrated and, if possible, validated using limited measured data. However, little research has been done to determine the impact of length of available calibration data on model parameterization and performance. The main objective of this study was to evaluate the impact of length of calibration data (LCD) on parameterization and performance of the Agricultural Policy Environmental eXtender model for predicting daily, monthly, and annual streamflow. Long‐term (1984‐2015) measured daily streamflow data from Rock Creek watershed, an agricultural watershed in northern Ohio, were used for this study. Data were divided into five Short (5‐year), two Medium (15‐year), and one Long (25‐year) streamflow calibration data scenarios. All LCD scenarios were calibrated and validated at three time steps: daily, monthly, and annual. Results showed LCD affected the ability of the model to accurately capture temporal variability in simulated streamflow. However, overall average streamflow, water budgets, and crop yields were simulated reasonably well for all LCD scenarios.     

Evaluation of groundwater suitability for domestic,irrigational, and industrial purposes: a case study from Thirumanimuttar river basin,Tamilnadu, India

The Thirumanimuttar sub-basin forms an important groundwater province in south India, facing serious deficiency in both quality and quantity of groundwater due to increased demand associated with rapid population explosion, agricultural growth and industrial activities. A total of 194 groundwater samples were collected and 15 water quality parameters were analyzed using standard procedures. Na⁺, Cl⁻, Ca^2 +, HCO₃^-_{3}^{-}, Mg^2 + and SO₄^2-_{4}^{2-} concentration ions are more dominant in both seasons. The total dissolved solids and electrical conductivity was observed good correlation with Na⁺, Cl⁻, HCO₃^-_{3}^{-}, Ca^2 +, Mg^2 +, Cl⁻, PO₄^3-_{4}^{3-} and NO₃^- _{3}^{- } ions indicating dominance of plagioclase feldspar weathering, anthropogenic input and over drafting of groundwater irrespective of seasons. The Hill–Piper diagram indicates alkaline earths exceed the alkalis, an increase of weak acids was noted during both the seasons. For assessing the groundwater for irrigation suitability parameters like total hardness, sodium adsorption ratio, residual sodium carbonate (RSC), permeability index, and sodium percentage are also calculated. Permanent hardness was noted in higher during both the seasons due to discharge of untreated effluents and ion exchange process. The RSC indicates 56% of the samples are not suitable for irrigation purposes in both seasons, if continuously used will affect the crop yield. From the results, nearly 72% of the samples are not suitable for irrigation.     

A study on evaluation of probable sources of heavy metal pollution in groundwater of Kalpakkam region, South India

This study investigates the heavy metal pollution vulnerability of the groundwater in the coastal aquifers of Kalpakkam region in the state of Tamilnadu, India. Integrated-approach includes pollution evaluation indices, principal component analysis (PCA), and correlation matrix (CM) to evaluate the intensity and source of pollution in groundwater. The data have been used for the calculation of heavy metal pollution index (HPI) and degree of contamination (C _d). The mean metal levels in groundwater followed a descending order as: Zn?>?Ba?>?Fe?>?Al?>?Se?>?Mn?>?Cu?>?Ni?>?Pb?>?Cr?>?Mo?>?As?>?Cd?>?Sb?>?Be. The concentrations of Fe, Cd, Zn, Se, Ba, Mn, Ni, Pb, and Al in some of the groundwater samples exceed the maximum admissible concentration (MAC). The HPI and C _d yield different results despite significant correlations between them. The following elemental associations were obtained from PCA and CM: Fe?CMn?CNi?CCr?CPb?CCd?CZn?CBe?CAl, Cu?CAs, Sb?CAs, Al?CBa and Se?CMo, which could be linked to anthropogenic sources (i.e., processes of tannery and dying industries with some contribution from the landfill leachate and municipal sewage). GIS-based factor score maps suggest that the activities of tannery industries and landfill leachate are pervasive processes in the area. This study has provided the evidence that effluents discharged from the tannery and auxiliary industries and landfill leachate are the main sources of heavy metal pollution in the groundwater. The high metal concentrations observed in the groundwater may have serious public health and potential environmental hazard implications.     

Water quality modeling of fertilizer management impacts on nitrate losses in tile drains at the field scale

Nitrate losses from subsurface tile drained row cropland in the Upper Midwest U.S. contribute to hypoxia in the Gulf of Mexico. Strategies are needed to reduce nitrate losses to the Mississippi River. This paper evaluates the effect of fertilizer rate and timing on nitrate losses in two (East and West) commercial row crop fields located in south-central Minnesota. The Agricultural Drainage and Pesticide Transport (ADAPT) model was calibrated and validated for monthly subsurface tile drain flow and nitrate losses for a period of 1999-2003. Good agreement was found between observed and predicted tile drain flow and nitrate losses during the calibration period, with Nash-Sutcliffe modeling efficiencies of 0.75 and 0.56, respectively. Better agreements were observed for the validation period. The calibrated model was then used to evaluate the effects of rate and timing of fertilizer application on nitrate losses with a 50-yr climatic record (1954-2003). Significant reductions in nitrate losses were predicted by reducing fertilizer application rates and changing timing. A 13% reduction in nitrate losses was predicted when fall fertilizer application rate was reduced from 180 to 123 kg/ha. A further 9% reduction in nitrate losses can be achieved when switching from fall to spring application. Larger reductions in nitrate losses would require changes in fertilizer rate and timing, as well as other practices such as changing tile drain spacings and/or depths, fall cover cropping, or conversion of crop land to pasture.     

Utilization of rice husk ash for the treatment of leachate generated from Perungudi semi-urban solid waste dumping site of Chennai city,Tamil Nadu,India

Journal of Material Cycles and Waste Management - Leachate from the secured landfill sites and open dumpsite seems to be threat to environment, which requires an immediate attention to investigate...