Hydrological Alteration Index as an Indicator of the Calibration Complexity of Water Quantity and Quality Modeling in the Context of Global Change

Modeling is a useful way to understand human and climate change impacts on the water resources of agricultural watersheds. Calibration and validation methodologies are crucial in forecasting assessments. This study explores the best calibration methodology depending on the level of hydrological alteration due to human-derived stressors. The Soil and Water Assessment Tool (SWAT) model is used to evaluate hydrology in South-West Europe in a context of intensive agriculture and water scarcity. The Index of Hydrological Alteration (IHA) is calculated using discharge observation data. A comparison of two SWAT calibration methodologies are done; a conventional calibration (CC) based on recorded in-stream water quality and quantity and an additional calibration (AC) adding crop managements practices. Even if the water quality and quantity trends are similar between CC and AC, water balance, irrigation and crop yields are different. In the context of rainfall decrease, water yield decreases in both CC and AC, while crop productions present opposite trends (+33% in CC and -31% in AC). Hydrological performance between CC and AC is correlated to IHA: When the level of IHA is under 80%, AC methodology is necessary. The combination of both calibrations appears essential to better constrain the model and to forecast the impact of climate change or anthropogenic influences on water resources

Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many ti..

Developing Guidelines for Two-Dimensional Model Review and Acceptance

Two independent modelers ran two hydraulic models, SRH-2D and HEC-RAS 2D. The models were applied to the Lakina River (MP 44 McCarthy Road) and to Quartz Creek (MP 0.7 Quartz Creek Road), which approximately represent straight and bend flow conditions, respectively. We compared the results, including water depth, depth averaged velocity, and bed shear stress, from the two models for both modelers. We found that the extent and density of survey data were insufficient for Quartz Creek. Neither model was calibrated due to the lack of basic field data (i.e., discharge, water surface elevation, and sediment characteristics). Consequently, we were unable to draw any conclusion about the accuracy of the models. Concerning the time step and the equations used (simplified or full) to solve the momentum equation in the HEC-RAS 2D model, we found that the minimum time step allowed by the model must be used if the diffusion wave equation is used in the simulations. A greater time step can be used if the full momentum equation is used in the simulations. We developed a set of guidelines for reviewing model results, and developed and provided a two-day training workshop on the two models for ADOT&PF hydraulic engineers

Spatiotemporal Calibration of Atmospheric Nitrogen Dioxide Concentration Estimates From an Air Quality Model for Connecticut

A spatiotemporal calibration and resolution refinement model was fitted to calibrate nitrogen dioxide (NO$_2$) concentration estimates from the Community Multiscale Air Quality (CMAQ) model, using two sources of observed data on NO$_2$ that differed in their spatial and temporal resolutions. To refine the spatial resolution of the CMAQ model estimates, we leveraged information using additional local covariates including total traffic volume within 2 km, population density, elevation, and land use characteristics. Predictions from this model greatly improved the bias in the CMAQ estimates, as observed by the much lower mean squared error (MSE) at the NO$_2$ monitor sites. The final model was used to predict the daily concentration of ambient NO$_2$ over the entire state of Connecticut on a grid with pixels of size 300 x 300 m. A comparison of the prediction map with a similar map for the CMAQ estimates showed marked improvement in the spatial resolution. The effect of local covariates was evident in the finer spatial resolution map, where the contribution of traffic on major highways to ambient NO$_2$ concentration stands out. An animation was also provided to show the change in the concentration of ambient NO$_2$ over space and time for 1994 and 1995.Comment: 23 pages, 8 figures, supplementary materia

DIRECT ESTIMATION OF ABOVEGROUND FOREST PRODUCTIVITY THROUGH HYPERSPECTRAL REMOTE SENSING OF CANOPY NITROGEN

The concentration of nitrogen in foliage has been related to rates of net photosynthesis across a wide range of plant species and functional groups and thus represents a simple and biologically meaningful link between terrestrial cycles of carbon and nitrogen. Although foliar N is used by ecosystem models to predict rates of leaf‐level photosynthesis, it has rarely been examined as a direct scalar to stand‐level carbon gain. Establishment of such relationships would greatly simplify the nature of forest C and N linkages, enhancing our ability to derive estimates of forest productivity at landscape to regional scales. Here, we report on a highly predictive relationship between whole‐canopy nitrogen concentration and aboveground forest productivity in diverse forested stands of varying age and species composition across the 360 000‐ha White Mountain National Forest, New Hampshire, USA. We also demonstrate that hyperspectral remote sensing can be used to estimate foliar N concentration, and hence forest production across a large number of contiguous images. Together these data suggest that canopy‐level N concentration is an important correlate of productivity in these forested systems, and that imaging spectrometry of canopy N can provide direct estimates of forest productivity across large landscapes

Estimation of surface longwave radiation components from ground-based historical net radiation and weather data

A methodology for estimating ground upwelling, clear-sky and cloud downwelling longwave radiations (L↑, Lsky ↓, and Lcld↓) and net shortwave radiation (Sn) at 30-min temporal scales based on long-term ground-based net radiations and meteorological observations is described. Components of surface radiation can be estimated from empirical models, cloud radiation models, and remote sensing observations. The proposed method combines the local calibration of empirical models and the radiative energy balance method to obtain the dual-directional, dual-spectral components of the surface radiation for the offline land surface process modeling and ecosystem study. By extracting information of radiation components from long-term net radiation and concurrent weather data, the utility of tower net radiation observations is maximized. Four test sites with multiyears' radiation records were used to evaluate the method. The results show that when compared with the results of empirical models using default parameters the proposed method is able to produce more accurate estimates of longwave surface components (Lg ↑, Lsky↓, Lcld↓) and net shortwave radiation (Sn). Overall, the estimated and observed surface radiation components show high correlations (>0.90), high index of agreement (>0.89), and low errors (root mean square error <30 W m-2 and bias <11 W m-2) at all of the test sites. The advantage of this scheme is that the refinement is achieved using the information from the historical net radiation and weather data at each observation site without additional measurements. This method is applicable for many existing observation sites worldwide which have long-term (at least 1 year) continuous net radiation records. Copyright 2008 by the American Geophysical Union

Used-habitat calibration plots: a new procedure for validating species distribution, resource selection, and step-selection models

“Species distribution modeling” was recently ranked as one of the top five “research fronts” in ecology and the environmental sciences by ISI's Essential Science Indicators (Renner and Warton 2013), reflecting the importance of predicting how species distributions will respond to anthropogenic change. Unfortunately, species distribution models (SDMs) often perform poorly when applied to novel environments. Compounding on this problem is the shortage of methods for evaluating SDMs (hence, we may be getting our predictions wrong and not even know it). Traditional methods for validating SDMs quantify a model's ability to classify locations as used or unused. Instead, we propose to focus on how well SDMs can predict the characteristics of used locations. This subtle shift in viewpoint leads to a more natural and informative evaluation and validation of models across the entire spectrum of SDMs. Through a series of examples, we show how simple graphical methods can help with three fundamental challenges of habitat modeling: identifying missing covariates, non-linearity, and multicollinearity. Identifying habitat characteristics that are not well-predicted by the model can provide insights into variables affecting the distribution of species, suggest appropriate model modifications, and ultimately improve the reliability and generality of conservation and management recommendations