Including spatial distribution in a data‐driven rainfall‐runoff model to improve reservoir inflow forecasting in Taiwan

Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd.     

Assessing regression‐based statistical approaches for downscaling precipitation over North America

This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.     

Preservation of Re‐Os isotope signatures in pyrite throughout low‐T,high‐P eclogite facies metamorphism

Pyrite in LT–HP eclogites from the western Tianshan orogenic belt yields a Re‐Os age of 378.1 ± 8.9 Ma, which is 30–70 Ma older than ages previously obtained for the same rocks using the Rb–Sr, Sm–Nd, Ar–Ar, U–Pb, and Lu–Hf isotope systems. The Tianshan LT–HP eclogite experienced temperatures of up to ~570 °C combined with pressures of up to 2.1 GPa during metamorphism. These conditions are below the transition of pyrite to pyrrhotite, which defines both pyrite stability and possibly its closure temperature for Re‐Os. Pyrite can preserve Re‐Os signatures through eclogite facies peak metamorphic conditions, and thus allow determination of the formation age of pyrite in the protolith.     

Holocene survival of the wild horse in Europe: a matter of open landscape?

The wild horse Equus ferus was one of the most frequent species of the Late Pleistocene large ungulate fauna in Eurasia and played an important role in the subsistence of human groups, especially at the end the Late Glacial. It is frequently assumed that E. ferus became extinct in Europe at the beginning of the Holocene because of the development of woodlands and loss of open habitats. Because of its preference for open habitats and in spite of its adaptability, the appearance or disappearance of the wild horse could therefore be a suitable palaeoecological indicator for the opening of the Holocene primeval woodlands. We revised the dating and reliability of the subfossil record and dated several bones by atomic mass spectrometry ¹⁴C dating. From the beginning of the Holocene (9600 cal a BC) to the end of the Atlantic Period (3750 cal a BC) there are 207 archaeological sites with wild horse records available in Europe. E. ferus survived the Pleistocene Holocene transition in Europe, but the spatiotemporal dynamics of populations fluctuated remarkably in the early and middle Holocene. Small and sparse populations increasingly became extinct during the early Holocene, until between 7100 and 5500 cal a BC the wild horse was almost absent in central parts of the European Lowlands. Particular conditions in natural open patches in the canopy forests, chalklands and floodplains may have maintained the local survival of the horse in some regions of the Lowlands, however. In the Late Atlantic, between 5500 and 3750 cal a BC the range of the wild horse was again extended. It re‐immigrated into central and western Europe, probably as a consequence of increasing landscape opening by Neolithic peoples. The data presented here may be a valuable part of the debate on the degree of openness of the early and middle Holocene landscape. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluating Lost Person Behavior Models

US wilderness search and rescue consumes thousands of person‐hours and millions of dollars annually. Timeliness is critical: the probability of success decreases substantially after 24 hours. Although over 90% of searches are quickly resolved by standard “reflex” tasks, the remainder require and reward intensive planning. Planning begins with a probability map showing where the lost person is likely to be found. The MapScore project described here provides a way to evaluate probability maps using actual historical searches. In this work we generated probability maps the Euclidean distance tables in (Koester 2008 ), and using Doke's ( 2012 ) watershed model. Watershed boundaries follow high terrain and may better reflect actual barriers to travel. We also created a third model using the joint distribution using Euclidean and watershed features. On a metric where random maps score 0 and perfect maps score 1, the Euclidean distance model scored 0.78 (95%CI: 0.74–0.82, on 376 cases). The simple watershed model by itself was clearly inferior at 0.61, but the Combined model was slightly better at 0.81 (95%CI: 0.77–0.84).     

Tidal Habitats Support Large Numbers of Invasive Blue Catfish in a Chesapeake Bay Subestuary

The introduction of a non-native freshwater fish, blue catfish Ictalurus furcatus, in tributaries of Chesapeake Bay resulted in the establishment of fisheries and in the expansion of the population into brackish habitats. Blue catfish are an invasive species in the Chesapeake Bay region, and efforts are underway to limit their impacts on native communities. Key characteristics of the population (population size, survival rates) are unknown, but such knowledge is useful in understanding the impact of blue catfish in estuarine systems. We estimated population size and survival rates of blue catfish in tidal habitats of the James River subestuary. We tagged 34,252 blue catfish during July–August 2012 and 2013; information from live recaptures (n = 1177) and dead recoveries (n = 279) were used to estimate annual survival rates and population size using Barker’s Model in a Robust Design and allowing for heterogeneity in detection probabilities. The blue catfish population in the 12-km study area was estimated to be 1.6 million fish in 2013 (95% confidence interval [CI] adjusted for overdispersion: 926,307–2,914,208 fish). Annual apparent survival rate estimates were low: 0.16 (95% CI 0.10–0.24) in 2012–2013 and 0.44 (95% CI 0.31–0.58) in 2013–2014 and represent losses from the population through mortality, permanent emigration, or both. The tagged fish included individuals that were large enough to exhibit piscivory and represented size classes that are likely to colonize estuarine habitats. The large population size that we estimated was unexpected for a freshwater fish in tidal habitats and highlights the need to effectively manage such species.