Statistically downscaled probabilistic multi‐model ensemble projections of precipitation change in a watershed

This paper presents the development of a probabilistic multi‐model ensemble of statistically downscaled future projections of precipitation of a watershed in New Zealand. Climate change research based on the point estimates of a single model is considered less reliable for decision making, and multiple realizations of a single model or outputs from multiple models are often preferred for such purposes. Similarly, a probabilistic approach is preferable over deterministic point estimates. In the area of statistical downscaling, no single technique is considered a universal solution. This is due to the fact that each of these techniques has some weaknesses, owing to its basic working principles. Moreover, watershed scale precipitation downscaling is quite challenging and is more prone to uncertainty issues than downscaling of other climatological variables. So, multi‐model statistical downscaling studies based on a probabilistic approach are required. In the current paper, results from the three well‐reputed statistical downscaling methods are used to develop a Bayesian weighted multi‐model ensemble. The three members of the downscaling ensemble of this study belong to the following three broad categories of statistical downscaling methods: (1) multiple linear regression, (2) multiple non‐linear regression, and (3) stochastic weather generator. The results obtained in this study show that the new strategy adopted here is promising because of many advantages it offers, e.g. it combines the outputs of multiple statistical downscaling methods, provides probabilistic downscaled climate change projections and enables the quantification of uncertainty in these projections. This will encourage any future attempts for combining the results of multiple statistical downscaling methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Honduras: Caribbean Coast

The coast of Honduras, Central America, represents the southern end of the Mesoamerican Barrier Reef System, although its marine resources are less extensive and studied than nearby Belize and Mexico. However, the coastal zone contains mainland reef formations, mangroves, wetlands, seagrass beds and extensive fringing reefs around its offshore islands, and has a key role in the economy of the country. Like most tropical areas, this complex of benthic habitats experiences limited annual variation in climatic and oceanographic conditions but seasonal and occasional conditions, particularly coral bleaching and hurricanes, are important influences. The effects of stochastic factors on the country's coral reefs were clearly demonstrated during 1998 when Honduras experienced a major hurricane and bleaching event. Any natural or anthropogenic impacts on reef health will inevitably affect other countries in Latin America, and vice versa, since the marine resources are linked via currents and the functioning of the system transcends political boundaries. Much further work on, for example, movement of larvae and transfer of pollutants is required to delineate the full extent of these links.

Anthropogenic impacts, largely driven by the increasing population and proportion of people living in coastal areas, are numerous and include key factors such as agricultural run-off, over-fishing, urban and industrial pollution (particularly sewage) and infrastructure development. Many of these threats act synergistically and, for example, poor watershed management via shifting cultivation, increases sedimentation and pesticide run-off onto coral reefs, which increases stress to corals already affected by decreasing water quality and coral bleaching. Threats from agriculture and fishing are particularly significant because of the size of both industries. The desire to generate urgently required revenue within Honduras has also led to increased tourism which provides an over-arching stress to marine resources since most tourists spend time in the coastal zone. Hence the last decade has seen a dramatic increase in coastal development, a greater requirement for sewage treatment and more demand for freshwater, particularly in the Bay Islands.

Although coastal zone management is relatively recent in Honduras, it is gaining momentum from both large-scale initiatives, such as the Ministry of Tourism's ‘Bay Islands Environmental Management Project', and national and international NGO projects. For example, a series of marine protected areas and legislative regulations have been established, but management capacity, enforcement and monitoring are limited by funding, expertise and training. Existing and future initiatives, supported by increased political will and environmental awareness of stakeholders, are vital for the long-term economic development of the country.     

Toward an Ecological Explanation of Agricultural Unemployment in Bangladesh

Cultural ecology theoretical framework was found suitable to explain unemployment in agriculture in three villages in Bangladesh. Path analysis and multiple regression statistics were used to examine the direct, indirect, and total effects of eight independent human, social, environmental, and technology variables as well as their aggregate contribution on agricultural unemployment. The multiple regression model explains 86.2% of the total variation in unemployment in agriculture; they were followed by environmental constraints and labor saving technology variables.     

A multi-proxy reconstruction of spatial and temporal variations in Asian summer temperatures over the last millennium

To investigate climate variability in Asia during the last millennium, the spatial and temporal evolution of summer (June–July–August; JJA) temperature in eastern and south-central Asia is reconstructed using multi-proxy records and the regularized expectation maximization (RegEM) algorithm with truncated total least squares (TTLS), under a point-by-point regression (PPR) framework. The temperature index reconstructions show that the late 20th century was the warmest period in Asia over the past millennium. The temperature field reconstructions illustrate that temperatures in central, eastern, and southern China during the 11th and 13th centuries, and in western Asia during the 12th century, were significantly higher than those in other regions, and comparable to levels in the 20th century. Except for the most recent warming, all identified warm events showed distinct regional expressions and none were uniform over the entire reconstruction area. The main finding of the study is that spatial temperature patterns have, on centennial time-scales, varied greatly over the last millennium. Moreover, seven climate model simulations, from the Coupled Model Intercomparison Project Phase 5 (CMIP5), over the same region of Asia, are all consistent with the temperature index reconstruction at the 99 % confidence level. Only spatial temperature patterns extracted as the first empirical orthogonal function (EOF) from the GISS-E2-R and MPI-ESM-P model simulations are significant and consistent with the temperature field reconstruction over the past millennium in Asia at the 90 % confidence level. This indicates that both the reconstruction and the simulations depict the temporal climate variability well over the past millennium. However, the spatial simulation or reconstruction capability of climate variability over the past millennium could be still limited. For reconstruction, some grid points do not pass validation tests and reveal the need for more proxies with high temporal resolution, accurate dating, and sensitive temperature signals, especially in central Asia and before AD 1400.     

Comparison between ordinary kriging (OK) and inverse distance weighted (IDW) based on estimation error. Case study: Dardevey iron ore deposit,NE Iran

Estimation of mineral resources and reserves with low values of error is essential in mineral exploration. The aim of this study is to compare inverse distance weighted (IDW) and ordinary kriging (OK) methods based on error estimation in the Dardevey iron ore deposit, NE Iran. Anisotropic ellipsoid and variograms were calculated and generated for estimation of Fe distribution by both methods. Density, continuity of ore and waste, the number of points involved, and the discretization factor in the estimation of ore and waste boundaries were determined and the resource estimated by IDW and OK methods. Estimation errors were classified based on JORC standard, and both methods were compared due to distribution of error estimation. Results obtained by the study indicate that error estimation of OK method is less than IDW method and that the results of OK method are reliable.     

Unnoticed but important: revealing the hidden contribution of community-based religious institution of the mosque in disasters

The role of community-based religious institutions has been largely undocumented, underestimated and overshadowed in the disaster studies literature. This paper explores the role of the mosque, a community-based religious institution, in disaster management by documenting and analysing its role in rural settings in the aftermath of the 2005 earthquake in Pakistan. The study examines the role of the mosque in relation to key actors from the state, civil society and private sector during response, relief, recovery, reconstruction and rehabilitation phases of the Pakistan earthquake. Using qualitative research methods and a case study design, this article analyses primary data collected through 5 months of fieldwork (in 2009 and 2010). The findings demonstrate the multifaceted and distinct contribution of the mosque in cultural, economic, social and political aspects of the lives of the earthquake-affected communities. Possible challenges to engagement with the mosque, both gender inclusiveness and political controversy around its role, are also raised. This research suggests that state, civil society and private sector actors involved in disaster management need to understand complex relationships involving people and their religious institutions, and their impact on the social dimension of recovery. The findings of the study contribute to the scarce knowledge about the role of community-based religious institutions including churches, mosques, synagogues and temples and call for engagement: that is, acknowledging and valuing their role for building a synergy between secular and religious efforts for disaster risk reduction and post-disaster recovery.     

Impact of rangeland enclosure and seasonal grazing on protected and unprotected rangelands in Chakwal region,Pakistan

Scrub rangelands support livestock grazing and provide ecosystem services to their inhabitants.The present study was conducted in Chakwal,an important tract of ...     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.