Estimating discharge from inundation imagery

Remote sensing from space-borne platforms is often seen as an appealing method of monitoring components of the hydrological cycle, including river discharge, due to its spatial coverage. However, data from these platforms is often less than ideal because the geophysical properties of interest are rarely measured directly and the measurements that are taken can be subject to significant errors. This study assimilated water levels derived from a TerraSAR-X synthetic aperture radar image and digital aerial photography with simulations from a two dimensional hydraulic model to estimate discharge, inundation extent, depths and velocities at the confluence of the rivers Severn and Avon, UK. An ensemble Kalman filter was used to assimilate spot heights water levels derived by intersecting shorelines from the imagery with a digital elevation model. Discharge was estimated from the ensemble of simulations using state augmentation and then compared with gauge data. Assimilating the real data reduced the error between analyzed mean water levels and levels from three gauging stations to less than 0.3 m, which is less than typically found in post event water marks data from the field at these scales. Measurement bias was evident, but the method still provided a means of improving estimates of discharge for high flows where gauge data are unavailable or of poor quality. Posterior estimates of discharge had standard deviations between 63.3 m3s-1 and 52.7 m3s-1, which were below 15% of the gauged flows along the reach. Therefore, assuming a roughness uncertainty of 0.03-0.05 and no model structural errors discharge could be estimated by the EnKF with accuracy similar to that arguably expected from gauging stations during flood events. Quality control prior to assimilation, where measurements were rejected for being in areas of high topographic slope or close to tall vegetation and trees, was found to be essential. The study demonstrates the potential, but also the significant limitations of currently available imagery to reduce discharge uncertainty in un-gauged or poorly gauged basins when combined with model simulations in a data assimilation framework.

Trees outside forests and rural livelihoods: a study of Chitwan District, Nepal

Trees outside forests (TOF) in Nepal’s Terai have significantly increased over the past decade. The Chitwan District was one of the focus districts in the Terai Community Forestry Development Project that promoted a tree seedling distribution program. This paper examines the current position of tree integration on farmland and its contribution to livelihoods of rural households in this district. Interviews with local key informants, government and non-government agencies and woodbased industries, as well as an in-depth study of 32 households were used to describe the constraints faced by the households in management of trees on farmland. Most households cited disease, poor growth, lack of preferred tree species, lack of technical support, an uncertain tree market, and lack of financial support as constraints. Despite the important role of trees in subsistence and marketbased rural livelihood diversification, and the consequent reduction in pressure on national forests from on-farm trees, current government policies and practices fail to recognise the value of these trees. It is argued that there is substantial potential for improving on-farm trees to enhance rural livelihoods. A responsive service mechanism centred on tree growing households would help the management of tree resources on the farmland.

Challenges in elevated CO2 experiments on forests

Current forest Free Air CO2 Enrichment (FACE) experiments are reaching completion. Therefore, it is time to define the scientific goals and priorities of future experimental facilities. In this opinion article, we discuss the following three overarching issues (i) What are the most urgent scientific questions and how can they be addressed? (ii) What forest ecosystems should be investigated? (iii) Which other climate change factors should be coupled with elevated CO2 concentrations in future experiments to better predict the effects of climate change? Plantations and natural forests can have conflicting purposes for high productivity and environmental protection. However, in both cases the assessment of carbon balance and how this will be affected by elevated CO2 concentrations and the interacting climate change factors is the most pressing priority for future experiments.

The impact of uncertainty in satellite data on the assessment of flood inundation models

The performance of flood inundation models is often assessed using satellite observed data; however these data have inherent uncertainty. In this study we assess the impact of this uncertainty when calibrating a flood inundation model (LISFLOOD-FP) for a flood event in December 2006 on the River Dee, North Wales, UK. The flood extent is delineated from an ERS-2 SAR image of the event using an active contour model (snake), and water levels at the flood margin calculated through intersection of the shoreline vector with LiDAR topographic data. Gauged water levels are used to create a reference water surface slope for comparison with the satellite-derived water levels. Residuals between the satellite observed data points and those from the reference line are spatially clustered into groups of similar values. We show that model calibration achieved using pattern matching of observed and predicted flood extent is negatively influenced by this spatial dependency in the data. By contrast, model calibration using water elevations produces realistic calibrated optimum friction parameters even when spatial dependency is present. To test the impact of removing spatial dependency a new method of evaluating flood inundation model performance is developed by using multiple random subsamples of the water surface elevation data points. By testing for spatial dependency using Moran’s I, multiple subsamples of water elevations that have no significant spatial dependency are selected. The model is then calibrated against these data and the results averaged. This gives a near identical result to calibration using spatially dependent data, but has the advantage of being a statistically robust assessment of model performance in which we can have more confidence. Moreover, by using the variations found in the subsamples of the observed data it is possible to assess the effects of observational uncertainty on the assessment of flooding risk.

The trees and the bees: using enforcement and income projects to protect forests and rural livelihoods through spatial joint production

Forest managers in developing countries enforce extraction restrictions to limit forest degradation. In response, villagers may displace some of their extraction to other forests, which generates “leakage” of degradation. Managers also implement poverty alleviation projects to compensate for lost resource access or to induce conservation. We develop a model of spatial joint production of bees and fuelwood that is based on forest-compatible projects such as beekeeping in Thailand, Tanzania, and Mexico. We demonstrate that managers can better determine the amount and pattern of degradation by choosing the location of both enforcement and the forest-based activity.

Protecting developing countries' forests: enforcement in theory and practice

This paper relates the key findings of the optimal economic enforcement literature to practical issues of enforcing forest and wildlife management access restrictions in developing countries. Our experiences, particularly from Tanzania and eastern India, provide detail of the key pragmatic issues facing those responsible for protecting natural resources. We identify large gaps in the theoretical literature that limit its ability to inform practical management, including issues of limited funding and cost recovery, multiple tiers of enforcement and the incentives facing enforcement officers, and conflict between protected area managers and rural people's needs.

Fine-root turnover rates of European forests revisited: an analysis of data from sequential coring and ingrowth cores

Background and Aims Forest trees directly contribute to carbon cycling in forest soils through the turnover of their fine roots. In this study we aimed to calculate root turnover rates of common European forest tree species and to compare them with most frequently published values. Methods We compiled available European data and applied various turnover rate calculation methods to the resulting database. We used Decision Matrix and Maximum-Minimum formula as suggested in the literature. Results Mean turnover rates obtained by the combination of sequential coring and Decision Matrix were 0.86 yr−1 for Fagus sylvatica and 0.88 yr−1 for Picea abies when maximum biomass data were used for the calculation, and 1.11 yr−1 for both species when mean biomass data were used. Using mean biomass rather than maximum resulted in about 30 % higher values of root turnover. Using the Decision Matrix to calculate turnover rate doubled the rates when compared to the Maximum-Minimum formula. The Decision Matrix, however, makes use of more input information than the Maximum-Minimum formula. Conclusions We propose that calculations using the Decision Matrix with mean biomass give the most reliable estimates of root turnover rates in European forests and should preferentially be used in models and C reporting.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.

REDD+ and community-controlled forests in low-income countries: any hope for a linkage?

Deforestation and forest degradation are estimated to account for between 12% and 20% of annual greenhouse gas emissions and in the 1990s (largely in the developing world) released about 5.8 Gt per year, which was bigger than all forms of transport combined. The idea behind REDD + is that payments for sequestering carbon can tip the economic balance away from loss of forests and in the process yield climate benefits. Recent analysis has suggested that developing country carbon sequestration can effectively compete with other climate investments as part of a cost effective climate policy. This paper focuses on opportunities and complications associated with bringing community-controlled forests into REDD +. About 25% of developing country forests are community controlled and therefore it is difficult to envision a successful REDD + without coming to terms with community controlled forests. It is widely agreed that REDD + offers opportunities to bring value to developing country forests, but there are also concerns driven by worries related to insecure and poorly defined community forest tenure, informed by often long histories of government unwillingness to meaningfully devolve to communities. Further, communities are complicated systems and it is therefore also of concern that REDD + could destabilize existing well-functioning community forestry systems.

Persistent reduced ecosystem respiration after insect disturbance in high elevation forests

Amid a worldwide increase in tree mortality, mountain pine beetles (Dendroctonus ponderosae Hopkins) have led to the death of billions of trees from Mexico to Alaska since 2000. This is predicted to have important carbon, water and energy balance feedbacks on the Earth system. Counter to current projections, we show that on a decadal scale, tree mortality causes no increase in ecosystem respiration from scales of several square metres up to an 84 km2 valley. Rather, we found comparable declines in both gross primary productivity and respiration suggesting little change in net flux, with a transitory recovery of respiration 6–7 years after mortality associated with increased incorporation of leaf litter C into soil organic matter, followed by further decline in years 8–10. The mechanism of the impact of tree mortality caused by these biotic disturbances is consistent with reduced input rather than increased output of carbon.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

Impact of a drier Early-Mid-Holocene climate upon Amazonian forests

This paper uses a palaeoecological approach to examine the impact of drier climatic conditions of the Early-Mid-Holocene (ca 8000-4000 years ago) upon Amazonia's forests and their fire regimes. Palaeovegetation (pollen data) and palaeofire (charcoal) records are synthesized from 20 sites within the present tropical forest biome, and the underlying causes of any emergent patterns or changes are explored by reference to independent palaeoclimate data and present-day patterns of precipitation, forest cover and fire activity across Amazonia. During the Early-Mid-Holocene, Andean cloud forest taxa were replaced by lowland tree taxa as the cloud base rose while lowland ecotonal areas, which are presently covered by evergreen rainforest, were instead dominated by savannahs and/or semi-deciduous dry forests. Elsewhere in the Amazon Basin there is considerable spatial and temporal variation in patterns of vegetation disturbance and fire, which probably reflects the complex heterogeneous patterns in precipitation and seasonality across the basin, and the interactions between climate change, drought- and fire susceptibility of the forests, and Palaeo-Indian land use. Our analysis shows that the forest biome in most parts of Amazonia appears to have been remarkably resilient to climatic conditions significantly drier than those of today, despite widespread evidence of forest burning. Only in ecotonal areas is there evidence of biome replacement in the Holocene. From this palaeoecological perspective, we argue against the Amazon forest 'dieback' scenario simulated for the future.