Balancing Uncertainty and Complexity to Incorporate Fire Spread in an Eco-Hydrological Model

Wildfire affects the ecosystem services of watersheds, and climate change will modify fire regimes and watershed dynamics. In many eco-hydrological simulations, fire is included as an exogenous force. Rarely are the bidirectional feedbacks between watersheds and fire regimes integrated in a simulation system because the eco-hydrological model predicts variables that are incompatible with the requirements of fire models. WMFire is a fire-spread model of intermediate complexity designed to be integrated with the Regional Hydro-ecological Simulation System (RHESSys). Spread in WMFire is based on four variables that (i) represent known influences on fire spread: litter load, relative moisture deficit, wind direction and topographic slope, and (ii) are derived directly from RHESSys outputs. The probability that a fire spreads from pixel to pixel depends on these variables as predicted by RHESSys. We tested a partial integration between WMFire and RHESSys on the Santa Fe (New Mexico) and the HJ Andrews (Oregon State) watersheds. Model assessment showed correspondence between expected spatial patterns of spread and seasonality in both watersheds. These results demonstrate the efficacy of an approach to link eco-hydrologic model outputs with a fire spread model. Future work will develop a fire effects module in RHESSys for a fully coupled, bidirectional model

Simulations of snow distribution and hydrology in a mountain basin

We applied a version of the Regional Hydro‐Ecologic Simulation System (RHESSys) that implements snow redistribution, elevation partitioning, and wind‐driven sublimation to Loch Vale Watershed (LVWS), an alpine‐subalpine Rocky Mountain catchment where snow accumulation and ablation dominate the hydrologic cycle. We compared simulated discharge to measured discharge and the simulated snow distribution to photogrammetrically rectified aerial (remotely sensed) images. Snow redistribution was governed by a topographic similarity index. We subdivided each hillslope into elevation bands that had homogeneous climate extrapolated from observed climate. We created a distributed wind speed field that was used in conjunction with daily measured wind speeds to estimate sublimation. Modeling snow redistribution was critical to estimating the timing and magnitude of discharge. Incorporating elevation partitioning improved estimated timing of discharge but did not improve patterns of snow cover since wind was the dominant controller of areal snow patterns. Simulating wind‐driven sublimation was necessary to predict moisture losses

Evapotranspiration deficit controls net primary production and growth of silver fir: Implications for Circum-Mediterranean forests under forecasted warmer and drier conditions

Warming-induced drought stress has been hypothesized as a major driver of forest net primary production (NPP) reduction, but we lack reliable field data to assess if higher temperatures lead to forest NPP reduction, particularly in humid sites and at basin to landscape spatial scales. The use of a landscape approach would allow considering the feedbacks operating between climate, topography, soil vegetation and water resources. Here we follow that approach by simulating NPP using the regional hydro-ecologic simulation system (RHESSys) model and by comparing the results with radial growth data (tree-ring widths and intrinsic water-use efficiency - iWUE). We evaluate the relationships between climate, growth, NPP, atmospheric CO2 concentrations (ca) and iWUE in xeric and mesic silver fir forests subjected to contrasting water balances. The growth data successfully validated the 11-month NPP cumulated until spring. The main negative climatic driver of growth and NPP was the summer evapotranspiration deficit, which shows a negative association with tree-ring width indices. Sensitivity analyses indicate that rising ca do not compensate the severe NPP reduction associated to warmer and drier conditions. The positive effect of rising ca on NPP is mediated by climatic site conditions being detected only in mesic sites, whereas the negative effects of drought on NPP override any ca-related enhancement of NPP in xeric sites. Future warmer and drier conditions causing a higher evaporative demand by the atmosphere could lead to a NPP decline in temperate conifer forests subjected to episodic droughts. © 2015 Elsevier B.V.We would like to thank the Spanish Meteorological State Agency (AEMET) and the Confederación Hidrográfica del Ebro for providing the climatic and streamflow databases used in this study. This work has been supported by research projects CGL2011-27574-CO2-02, CGL2011-27536, CGL2014-52135-CO3-01 and Red de variabilidad y cambio climático RECLIM (CGL2014-517221-REDT) financed by the Spanish Commission of Science and Technology and FEDER, “LIFE12 ENV/ES/000536-Demonstration and validation of innovative methodology for regional climate change adaptation in the Mediterranean area (LIFE MEDACC)” financed by the LIFE programme of the European Commission and CTTP1/12 financed by the Comunidad de Trabajo de los Pirineos. JJC also acknowledges the support of ARAID and projects 012/2008, 387/2011 and 1012S (Organismo Autónomo Parques Nacionales, Spain).Peer Reviewe

Energy, water, and carbon fluxes in a loblolly pine stand: Results from uniform and gappy canopy models with comparisons to eddy flux data

1] This study investigates the impacts of canopy structure specification on modeling net radiation (R n), latent heat flux (LE) and net photosynthesis (A n) by coupling two contrasting radiation transfer models with a two-leaf photosynthesis model for a maturing loblolly pine stand near Durham, North Carolina, USA. The first radiation transfer model is based on a uniform canopy representation (UCR) that assumes leaves are randomly distributed within the canopy, and the second radiation transfer model is based on a gappy canopy representation (GCR) in which leaves are clumped into individual crowns, thereby forming gaps between the crowns. To isolate the effects of canopy structure on model results, we used identical model parameters taken from the literature for both models. Canopy structure has great impact on energy distribution between the canopy and the forest floor. Comparing the model results, UCR produced lower R n , higher LE and higher A n than GCR. UCR intercepted more shortwave radiation inside the canopy, thus producing less radiation absorption on the forest floor and in turn lower R n . There is a higher degree of nonlinearity between A n estimated by UCR and by GCR than for LE. Most of the difference for LE and A n between UCR and GCR occurred around noon, when gaps between crowns can be seen from the direction of the incident sunbeam. Comparing with eddy-covariance measurements in the same loblolly pine stand from May to September 2001, based on several measures GCR provided more accurate estimates for R n , LE and A n than UCR. The improvements when using GCR were much clearer when comparing the daytime trend of LE and A n for the growing season. Sensitivity analysis showed that UCR produces higher LE and A n estimates than GCR for canopy cover ranging from 0.2 to 0.8. There is a high degree of nonlinearity in the relationship between UCR estimates for A n and those of GCR, particularly when canopy cover is low, and suggests that simple scaling of UCR parameters cannot compensate for differences between the two models. LE from UCR and GCR is also nonlinearly related when canopy cover is low, but the nonlinearity quickly disappears as canopy cover increases, such that LE from UCR and GCR are linearly related and the relationship becomes stronger as canopy cover increases. These results suggest the uniform canopy assumption can lead to underestimation of R n , and overestimation of LE and A n . Given the potential in mapping regional scale forest canopy structure with high spatial resolution optical and Lidar remote sensing plotforms, it is possible to use GCR for up-scaling ecosystem processes from flux tower measurements to heterogeneous landscapes, provided the heterogeneity is not too extreme to modify the flow dynamics., Energy, water, and carbon fluxes in a loblolly pine stand: Results from uniform and gappy canopy models with comparisons to eddy flux data, J. Geophys. Res., 114, G04021, doi:10.1029/2009JG000951

Adding our leaves: A communityâ wide perspective on research directions in ecohydrology

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154539/1/hyp13693.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154539/2/hyp13693_am.pd

BioEarth: Envisioning and developing a new regional earth system model to inform natural and agricultural resource management

As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers’ needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and “usability” of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatial-scale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making

Regional regression models of percentile flows for the contiguous United States: Expert versus data-driven independent variable selection

Study region: 918 basins in the contiguous United States. Study focus: Regional regression models were developed to predict 13 percentile flows for groups of basins clustered based on physical and climatic characteristics. The research question investigated how the number and information content of independent variables affected model performance, and compared data-driven versus expert assessment approaches for variable selection. New hydrological insights for the region: A set of three variables selected based on an expert assessment of factors that influence percentile flows performed similarly to larger sets of variables selected using a data-driven method. Expert assessment variables included mean annual precipitation, potential evapotranspiration, and baseflow index. Larger sets of up to 37 variables contributed little, if any, additional predictive information. Variables used to describe the distribution of basin data (e.g. standard deviation) were not useful, and average values were sufficient to characterize physical and climatic basin conditions. Effectiveness of the expert assessment variables may be due to the high degree of multicollinearity (i.e. cross-correlation) among additional variables. A tool is provided in the Supplementary material to predict percentile flows based on the three expert assessment variables. Future work should develop new variables with a strong understanding of the processes related to percentile flows