Modeling Postfire Response and Recovery using the Hydrologic Engineering Center Hydrologic Modeling System (HEC‐HMS)1

Abstract: This paper investigates application of the Army Corps of Engineers’ Hydrologic Engineering Center Hydrologic Modeling System (HEC‐HMS) to a burned watershed in San Bernardino County, California. We evaluate the HEC‐HMS’ ability to simulate discharge in prefire and postfire conditions in a semi arid watershed and the necessary parameterizations for modeling hydrologic response during the immediate, and subsequent recovery, period after a wildfire. The model is applied to City Creek watershed, which was 90% burned during the Old Fire of October 2003. An optimal spatial resolution for the HEC‐HMS model was chosen based on an initial sensitivity analysis of subbasin configurations and related model performance. Five prefire storms were calibrated for the selected model resolution, defining a set of parameters that reasonably simulate prefire conditions. Six postfire storms, two from each of the following rainy (winter) seasons were then selected to simulate postfire response and evaluate relative changes in parameter values and model behavior. There were clear trends in the postfire parameters [initial abstractions (I_a), curve number (CN), and lag time] that reveal significant (and expected) changes in watershed behavior. CN returns to prefire (baseline) values by the end of Year 2, while I_a approaches baseline by the end of the third rainy season. However, lag time remains significantly lower than prefire values throughout the three‐year study period. Our results indicate that recovery of soil conditions and related runoff response is not entirely evidenced by the end of the study period (three rainy seasons postfire). Understanding the evolution of the land surface and related hydrologic properties during the highly dynamic postfire period, and accounting for these changes in model parameterizations, will allow for more accurate and reliable discharge simulations in both the immediate, and subsequent, rainy seasons following fire.     

Adaptive Management of Stream Channel Maintenance at Bridge Crossings in the Northern Tier Region,Pennsylvania1

Abstract: An adaptive management framework is applied to the problem of identifying mitigation measures for sediment deposition near bridge crossings in small streams in the Northern Tier region of northern Pennsylvania. The presence of the rigid bridge infrastructure introduces a challenge for applying adaptive management practices, because the integrity of the bridge structure itself has to be maintained regardless of the mitigation practices used in the stream channel near the bridge. In an effort to overcome the unacceptable risk that field‐scale adaptive management experiments present to rigid bridge infrastructure, an adaptive management approach for laboratory‐scale experimentation of mitigation methods at bridge crossings in the Northern Tier region is presented as a way to decrease the level of uncertainty about channel response to mitigation measures and increase the rate of learning about the effectiveness of these measures. Four cycles of adaptive management experiments are discussed to demonstrate that this approach results in fast and efficient learning about channel response to mitigation methods for the given conditions. The value of monitoring and of assessment of monitored data in the overall efficiency of the adaptive management approach is highlighted. Assessment of what was learned in the adaptive management experiment cycles presented here leads to new directions to continually improve management policies and practices in stream channels at bridge crossings in the Northern Tier region. The adaptive management process, rather than continuing with a normally risk‐averse management approach, results in opportunities for learning new information about a system’s response.     

Fast firing of tiles containing paper mill sludge, glass cullet and clay

The paper describes results obtained in the development of a previous research. We study here, in fast firing, the sintering behaviour and measure some properties of tiles containing a mixture of 60 wt% of paper mill sludge and 40 wt% of glass cullet. The behaviour of this material is compared to those displayed by materials obtained by the same mixture added with 10, 20 and 30 wt% of a natural red clay. In parallel, the same properties are measured also on a reference blend, which is presently used to produce commercial tiles. We show that powders containing 60 wt% of paper sludge and 40 wt% of glass cullet to which 30 wt% of clay is added give rise to materials that display a stable sintering process and have good hardness and strength and therefore could be used for the industrial production of tiles.     

Investigations on CO2 storage capacity in saline aquifers—Part 2: Estimation of storage capacity coefficients

Many studies on geological carbon dioxide (CO₂) storage capacity neglect the influence of complex coupled processes which occur during and after the injection of CO₂. Storage capacity is often overestimated since parts of the reservoirs cannot be reached by the CO₂ plume due to gravity segregation and are thus not accessible for storage. This work investigates the effect of reservoir parameters like depth, temperature, absolute and relative permeability, and capillary pressure on the processes during CO₂ injection and thus on estimates of effective storage capacity. The applied statistical characteristics of parameters are based on a large reservoir parameter database. Different measured relative permeability relations are considered. The methodology of estimating storage capacity is discussed. Using numerical 1D and 3D experiments, detailed time-dependent storage capacity estimates are derived. With respect to the concept developed in this work, it is possible to estimate effective CO₂ storage capacity in saline aquifers. It is shown that effective CO₂ mass stored in the reservoir varies by a factor of 20 for the reservoir setups considered. A high influence of the relative permeability relation on storage capacity is shown.