Journal Paper Digests 2020 #19
- Operational Bayesian GLS Regression for Regional Hydrologic Analyses
- The Role of Fire in the Coevolution of Soils and Temperate Forests
- A Calibration Framework for High‐Resolution Hydrological Models Using a Multiresolution and Heterogeneous Strategy
- Bootstrap Aggregation and Cross‐Validation Methods to Reduce Overfitting in Reservoir Control Policy Search
- Developing pedotransfer functions to harmonize extractable soil phosphorus content measured with different methods: A case study across the mainland of France
- Legacy data-based national-scale digital mapping of key soil properties in India
- Estimating Atterberg limits of soils from hygroscopic water content
- Characterization of the water retention curves of Everglades wetland soils
- Constant carbon pricing increases support for climate action compared to ramping up costs over time
Operational Bayesian GLS Regression for Regional Hydrologic Analyses
Dirceu S. Reis Jr Andrea G. Veilleux Jonathan R. Lamontagne Jery R. Stedinger Eduardo S. Martins
e2019WR026940 First Published:19 February 2020
- A comprehensive statistical framework for regional hydrologic regression is presented with spatially correlated flow data and varying record lengths *Framework correctly attributes variability to sampling errors in computed statistic, variability explained by the model, and model error *New diagnostics includes Bayesian plausibility value, pseudo adjusted R2, pseudo ANOVA, and Bayesian metrics for leverage and influence
A Calibration Framework for High‐Resolution Hydrological Models Using a Multiresolution and Heterogeneous Strategy
Ruochen Sun Felipe Hernández Xu Liang Huiling Yuan e2019WR026541 First Published:12 June 2020 Key Points
- A novel framework is developed for automatic calibration of computationally demanding hydrological models with a large number of parameters
- The framework alleviates equifinality, and calibrated parameters achieve more reasonable values that better correspond to physical reality
- The framework leads to faster improvement and smoother convergence to optimal objective values and is tested with 134 calibration parameters
The Role of Fire in the Coevolution of Soils and Temperate Forests
Assaf Inbar Petter Nyman Patrick N. J. Lane Gary J. Sheridan e2019WR026005 First Published:11 July 2020 Key Points
- Wildfire strongly influences forest hydrogeomorphology, yet its net effect on coevolution of soil‐vegetation systems is not yet constrained
- We used a new numerical model to test the hypothesis that fire is central to coevolution of forest‐soil systems in SE Australia
- The hypothesis was supported, and the role of fire was found to increase with aridity due to higher fire frequency and less developed soils
Bootstrap Aggregation and Cross‐Validation Methods to Reduce Overfitting in Reservoir Control Policy Search
Zachary P. Brodeur Jonathan D. Herman Scott Steinschneider e2020WR027184 First Published:08 July 2020 Key Points
- We apply machine learning techniques of bootstrap aggregation (bagging) and cross‐validation to improve reservoir control policy search
- Block bootstrapping of historic hydrology based on paleo‐inflows can efficiently generate calibration‐validation‐testing data
- Policy selection according to validation performance on bootstrapped data leads to the greatest improvement in out‐of‐sample performance
Developing pedotransfer functions to harmonize extractable soil phosphorus content measured with different methods: A case study across the mainland of France
Phosphorus (P) is a nutrient essential to living organisms and ecosystems. Accurate information regarding extractable soil P is necessary for agricultural management and environmental quality. Direct measurements of extractable soil P at large scales are usually impeded by considerable time, labour, and economic resources required for implementation. To meet agronomic and environmental monitoring needs, multiple extraction methods have been developed worldwide to estimate the different components of soil P. In France, three extraction methods are used, namely the Dyer method for acidic soils, Joret-Hébert for calcareous soils, and Olsen for all soils. Therefore, it is difficult to compare data obtained nationwide for monitoring purposes. Consequently, it is of significant importance to develop pedotransfer functions (PTFs) to harmonise extractable soil P data obtained from different extraction methods with the assistance of other easily available predictors from soil information systems. In this study, we used an extensive dataset from the French soil-monitoring programme for the calibration and evaluation of PTFs. We implemented the partial least squares regression to relate extractable P measured by the Dyer or Joret-Hébert method to extractable P determined by the Olsen method considering 14 soil properties (total P2O5, pH, cation exchange capacity (CEC), CaCO3, soil texture (clay, silt and sand contents), total organic carbon, and exchangeable Fe, Al, CaO, Mn, MgO, and K2O). We constructed patrimonial models by selecting the most important predictors. According to the results of 10 iterations cross-validation, the average R2, root mean-square error (RMSE), and mean error (ME) of the PTF of calcareous soils were 0.66, 25.81, and −0.11 mg kg−1, whereas those of acidic soils were 0.70, 24.02, and −0.87 mg kg1, respectively. The Joret-Hébert P2O5, silt, pH, total P2O5, CEC, and K were the most important predictors for estimating Olsen P2O5 in calcareous soils, whereas Dyer P2O5, exchangeable Al, K, and pH were the most important predictors for estimating Olsen P2O5 in acidic soils. We observed that the explanatory power of the soil properties was more important in calcareous than in acidic soils. As expected, the proxies of Olsen P2O5, namely, Dyer P2O5 and Joret-Hébert P2O5, were the most important variables in modelling Olsen P2O5 variations. In addition, the relationship between Olsen P2O5 and Dyer P2O5 was much stronger than that between Olsen P2O5 and Joret-Hébert P2O5. The results confirmed the feasibility of estimating extractable P in soil by PTFs that were constructed using statistical methods, such as partial least squares regression. The addition of more predictors that are related to agricultural practices and topography attributes may improve the prediction accuracy.
Legacy data-based national-scale digital mapping of key soil properties in India
Abstract Mapping soil resources at a national scale in large countries such as India is a challenge because of limited soil data available and efforts to collect them. Legacy soil information shows promise; but, there are still challenges that need to be addressed. In this study, we deliver the first digital maps of key soil properties down to 2 m depth across India using legacy data and quantified the relationships between mapped soil properties and bioclimatic and terrain attributes. A legacy database containing analytical data for 1,707 soil profiles with 7,337 soil horizons was collated from reports published by the National Bureau of Soil Survey and Land Use Planning and other Indian organizations. 3D regression kriging based on the random forest model was used to map sand and clay contents, pH and soil organic carbon (SOC) contents at depths as per the GlobalSoilMap specifications. Important covariates included mean monthly temperature and precipitation data from the World Climatic Centre and terrain attributes derived from the NASA’s Shuttle Radar Topography Mission (SRTM) digital elevation model. The uncertainty of the model was quantified as the coefficient of variation of the predicted soil properties by repeated random sampling of the profile dataset into calibration and validation samples at a ratio of 75:25. The performance statistics for the surface soil properties were superior to subsurface soils with the highest Lin’s concordance coefficient (LCC) recorded for soil pH. Estimated LCC values in validation datasets ranged from 0.81 to 0.84 for pH, 0.30 to 0.59 for SOC, 0.48 to 0.56 for clay content, and 0.34 to 0.44 for sand content. Elevation, topographic wetness index, high rainfall, and temperature were observed to be the major drivers for the variability of selected soil properties. Prepared digital soil maps across different agroecological regions showed that sandy soils dominated Western Plains while clayey soils were dominant in the central Deccan Plateau. Soils of the North-Eastern hills were acidic in nature while the Western Ghats and Coastal Plains showed high SOC accumulation. Although finer soil fractions are considered as major drivers of SOC stabilization, rainfall during June (onset of monsoon) was a major climatic driver for SOC in Indian soils. The national maps of soil properties may be linked to soil productivity and provisioning of ecosystem services to guide policy makers for creating region-specific soil management plans.
Estimating Atterberg limits of soils from hygroscopic water content
Abstract A number of environmental, agronomic and engineering applications require knowledge of the Atterberg limits (liquid limit, LL; plastic limit, PL) and the plasticity index, PI of soils. The tedious and costly nature of standard experimental methods, as well as challenges with measurement repeatability motivated the development of regressions as well as more sophisticated techniques to estimate the Atterberg limits from other properties such as clay content, cation exchange capacity (CEC), and soil specific surface area. The amount of water adsorbed to particle surfaces at relative humidity (RH) < 95% is intimately linked to these soil properties, which suggests that hygroscopic water content (wh) may be a better predictor of the Atterberg limits. The present study (i) proposes regression models that estimate the LL, PL, and PI from wh at different relative humidity values ranging from 10 to 90% and considering water sorption hysteresis, and (ii) compares the performance of the models to other models that comprise clay, silt and organic carbon contents and CEC. For model development, wh was measured by water adsorption and desorption for 168 soil samples that varied widely in terms of geographic origin, clay mineralogy, and soil organic carbon content. The LL and PL were determined with the drop cone penetrometer and rolling device, respectively. Regression models were developed for both sorption directions for nine RH values between 10 and 90%. For 44 independent soil samples, the models estimated LL, PL and PI accurately (e.g., for desorption wh measured at 90% RH, RMSE and r2 values were 6.43% & 0.89; 3.95% & 0.83 and 6.69% & 0.79, respectively). There was no clear effect of sorption direction on the estimation accuracy. The wh determined at higher RH tended to better estimate the Atterberg limits compared to that measured at lower RH. The wh models were superior in estimating LL and PL compared to models that were based on clay content and organic carbon or CEC. For the PI, the models based on CEC performed slightly better than the wh models. Thus, a single measure of wh can provide reliable estimates of the Atterberg limits and PI.
Characterization of the water retention curves of Everglades wetland soils
Abstract Alterations to the natural hydrology of wetlands like the Everglades have increased the presence of unsaturated zones in typically inundated soil environments. Understanding soil moisture dynamics through numerical modeling requires a large dataset of soil hydraulic parameters (SHPs) due to the spatially variable nature of hydromorphic soils typically found in wetlands. Furthermore, the application of conventional parameterization models is challenging due to shrinkage with desaturation observed in these organic-rich soils. The objective of this work was to investigate water retention, shrinkage, and the effect of shrinkage on the SHPs of Everglades soils. This study used pressure plate extractors to determine the soil water retention curves (SWRCs) and corresponding shrinkage from triplicates for 53 sites across the Everglades. In addition, the organic content (OC), and fiber content (FC) were measured in the laboratory. An agglomerative clustering method applied to the retention and shrinkage data identified three distinct clusters: marl, mixed marl-peat and peat. Marl had higher volumetric water contents (VWCs) than marl-peat or peat particularly at higher pressures. The susceptibility of peat to shrinkage resulted in lower VWCs compared to the other two soils. When shrinkage was accounted for in the vGM model, a significantly higher and lower were observed. In addition, some deviations from typical SWRC behavior, attributed to the collapse of macro-pores, caused poor model fit. Northern marshes with predominantly fibric peat had steeper SWRCs due to the inverse relationship between FC and VWC at higher pressures. Further work is required to determine the influence of vegetation on the SWRC.
Constant carbon pricing increases support for climate action compared to ramping up costs over time
Michael M. Bechtel, Kenneth F. Scheve & Elisabeth van Lieshout Nature Climate Change volume 10, pages1004–1009(2020)
Abstract The introduction of policies that increase the price of carbon is central to limiting the adverse effects of global warming. Conventional wisdom holds that, of the possible cost paths, gradually raising costs relating to climate action will receive the most public support. Here, we explore mass support for dynamic cost paths in four major economies (France, Germany, the United Kingdom and the United States). We find that, for a given level of average costs, increasing cost paths receive little support whereas constant cost schedules are backed by majorities in all countries irrespective of whether those average costs are low or high. Experimental evidence indicates that constant cost paths significantly reduce opposition to climate action relative to increasing cost paths. Preferences for climate cost paths are related to the time horizons of individuals and their desire to smooth consumption over time.