Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Large groundwater level (GWL) data sets are often patchy with hydrographs containing continuous gaps and irregular measurement frequencies. However, most statistical time series analyses require regular observations, thus hydrographs with larger gaps are routinely excluded from further analysis despite the loss of coverage and representativity of an initially large data set. Missing values can be filled in with different imputation methods, yet the challenge is to assess the imputation performance of automated methods. Assessment of such methods tends to be carried out on randomly introduced missing values. However, large GWL data sets are commonly dominated by more complex patterns of missing values with longer contiguous gaps. This study presents a new artificial gap introduction approach (TGP- typical gap patterns) that improves our understanding of automated imputation performance by mimicking typical gap patterns found in regional scale groundwater hydrographs. Imputation performance of machine learning algorithm missForest and imputePCA is then compared with commonly applied linear interpolation to prepare a gapless daily GWL data set for the Baltic states (Estonia, Latvia, Lithuania). We observed that imputation performance varies among different gap patterns, and performance for all imputation algorithms declined when infilling previously unseen extremes and hydrographs influenced by groundwater abstraction. Further, missForest algorithm substantially outperformed other methods when infilling contiguous gaps (up to 2.5 years), while linear interpolation performs similarly for short random gaps. The TGP approach can be of use to assess the complexity of missing observation patterns in a data set and its value lies in assessing the performance of gap filling methods in a more realistic way. Thus the approach aids the appropriate selection of imputation methods, a task not limited to groundwater level time series alone. The study further provides insights into region-specific data peculiarities that can assist groundwater analysis and modelling

Efficacy of statistical algorithms in imputing missing data of streamflow discharge imparted with variegated variances and seasonalities

Streamflow missing data rises to a real challenge for calibration and validation of hydrological models as well as for statistically based methods of streamflow prediction. Although several algorithms have been developed thus far to impute missing values of hydro(geo)logical time series, the effectiveness of methods in imputation when the time series are influenced by different seasonalities and variances have remained largely unexplored. Therefore, we evaluated the efficacy of five different statistical algorithms in imputation of streamflow and groundwater level missing data under variegated periodicities and variances. Our performance evaluation is based on the streamflow data, procured from a hydrological model, and the observed groundwater data from the federal state of Brandenburg in Northeast Germany. Our findings revealed that imputations methods embodying the time series nature of the data (i.e., preceding value, autoregressive integrated moving average (ARIMA), and autoregressive conditional heteroscedasticity model (ARCH)) resulted in MSEs (Mean Squared Error) that are between 20 and 40 times smaller than the MSEs obtained from the Ordinary least squares (OLS) regression, which do not consider this quality. ARCH and ARIMA excelled in imputing missing values for hydrological time series, specifically for the streamflow and groundwater level data. ARCH outperformed ARIMA in both the streamflow and groundwater imputation under various conditions, such as without seasonality, with seasonality, low and high variance, and high variance (white noise) conditions. For the streamflow data, ARCH achieved average MSEs of 0.0000704 and 0.0003487 and average NSEs of 0.9957710 and 0.9965222 under without seasonality and high variance conditions, respectively. Similarly, for the groundwater level data, ARCH demonstrated its capability with average MSEs of 0.000635040 and average NSEs of 0.9971351 under GWBR1 condition. The effectiveness of ARCH, originated from econometric time series methods, should be further assessed by other hydro(geo)logical time series obtained from different climate zones

Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series

Real-time soil matric potential measurements for determining potato production's water availability are currently used in precision irrigation. It is well known that managing irrigation based on soil matric potential (SMP) helps increase water use efficiency and reduce crop environmental impact. Yet, SMP monitoring presents challenges and sometimes leads to gaps in the collected data. This research sought to address these data gaps in the SMP time series. Using meteorological and field measurements, we developed a filtering and imputation algorithm by implementing three prominent predictive models in the algorithm to estimate missing values. Over 2 months, we gathered hourly SMP values from a field north of the Péribonka River in Lac-Saint-Jean, Québec, Canada. Our study evaluated various data input combinations, including only meteorological data, SMP measurements, or a mix of both. The Extreme Learning Machine (ELM) model proved the most effective among the tested models. It outperformed the k-Nearest Neighbors (kNN) model and the Evolutionary Optimized Inverse Distance Method (gaIDW). The ELM model, with five inputs comprising SMP measurements, achieved a correlation coefficient of 0.992, a root-mean-square error of 0.164 cm, a mean absolute error of 0.122 cm, and a Nash-Sutcliffe efficiency of 0.983. The ELM model requires at least five inputs to achieve the best results in the study context. These can be meteorological inputs like relative humidity, dew temperature, land inputs, or a combination of both. The results were within 5% of the best-performing input combination we identified earlier. To mitigate the computational demands of these models, a quicker baseline model can be used for initial input filtering. With this method, we expect the output from simpler models such as gaIDW and kNN to vary by no more than 20%. Nevertheless, this discrepancy can be efficiently managed by leveraging more sophisticated models

Integrated modelling of water security in data-sparse regions under uncertainty

Freshwater scarcity and sustainability is one of the most complicated and difficult issues the world is currently facing, and it has been identified as a global concern. According to expert studies, 80% of the world’s population is projected to live in freshwater threats due to a plethora of factors viz., rapid population growth, urbanization, global climate change resulting from spatial and temporal changes in magnitude, frequencies and intensity of precipitation and temperature which leads to the transformation of the hydrologic cycle. Recent initiatives, including sustainable development goals, have been made to address these problems and offer solutions. However, the quantity and quality of freshwater systems and resources must be objectively and comprehensively understood and assessed at the scale of river basins to provide sufficient mitigation and resilience planning. Hydrologic modelling has been one the most suitable and efficient strategies for basin-scale assessment of freshwater dynamics to current and projected climate change and the focus has been on the application of traditional modelling framework which is tenable where data requirements are sufficient to couple hydrologic models with atmospheric data to account for climate change. The aforementioned strategy is a challenge in regions with inadequate ground-based observations necessary for climate and hydrologic modelling. The rarely available data in such regions may have repetitive gaps of missing data points with negative consequences including biased statistical representation of basin climatic features, ineffective model calibration and unreliable timing of peak flows which may amplify the uncertainties of the hydrologic dynamics leading to flawed depictions of watershed responses. Recently, integrated strategies are evolving that couple hydrologic models with climate data in water resource studies to account for uncertainties through the use of alternative data sources of many spatial climate data products from climate research centres to overcome the identified challenges. This research developed and applied a multi-criteria approach to examine the efficacy of gridded climate products using different performance metrics, a machine learning-based approach, Boruta random forest (BRF) to assess multiple GCM datasets required for hydro climatic studies and an integrated BRF-SWAT technique to define the relationship between the hydrologic variables and improve rainfall-runoff modelling in a data-sparse and climate sensitive watersheds. The developed model was applied to assess the projected green and blue water dynamics and sustainability in the Yobe-Komadugu basin of the greater Lake Chad, a watershed that is prone to extreme events (SPEI of flood and drought hazards). The results demonstrate that though the performance of the gridded data varies in space and time, multi-criteria assessment enhances the choice of a product with reduced uncertainty for climate modelling. The incorporation of the BRF approach in GCM evaluation indicates a consistent spatial and temporal representation of the climatological features with suitable mean correlation (R2 = 0.95), reduced mean annual precipitation bias of 0.69 mm/year and enhanced statistical trend and magnitude of the SPEI drought and flood hazards relative to identified and tested approaches from the literature. The integrated framework of the rainfall-runoff modelling strategy indicated that the hydrologic fluxes can be simulated fairly accurately with varying degrees of acceptability, irrespective of the watershed morphological properties, although there are significant trade-offs in model parameter sensitivity. The availability of satellite-based measurements of hydrologic fluxes and states, coupled with a machine learning feature selection and data refinement process has made integrated water balance modelling widely seen as a viable alternative for improving watershed hydrologic processes in data-sparse regions within acceptable uncertainty limits. Furthermore, the sub-watershed assessment of the projected changes in spatial and temporal green and blue water sustainability status has shown that the sub-basins will be ecologically fragile, and the identified freshwater geographic hotspots may be beyond restoration without adequate long-term river basin water resources plans. The modelling framework developed is, however, independent of the model and data type and can be applied to watersheds with similar modelling challenges. This study has provided a pathway or methods for managing and securing water resources information as a decision support tool to guarantee ongoing watershed monitoring and assessment of water security even in the face of increasingly unpredictable future circumstances in data-sparse watersheds that take into account uncertainty and chat a course for prospective risk assessment or the possibility and understanding that a certain effect brought on by climate-induced hazards would prevail in watershed freshwater sustainability. Therefore, it is essential to comprehend the constraints associated with forecasting changes in the water cycle to improve the climate and hydrologic modelling process, which is required to create effective strategies for adapting to climate change-related water resource hazards. Even in the face of severe uncertainty about the future, this will be essential in addressing concerns related to water security and management and promoting the climatic resilience of ecosystems and society.Freshwater scarcity and sustainability is one of the most complicated and difficult issues the world is currently facing, and it has been identified as a global concern. According to expert studies, 80% of the world’s population is projected to live in freshwater threats due to a plethora of factors viz., rapid population growth, urbanization, global climate change resulting from spatial and temporal changes in magnitude, frequencies and intensity of precipitation and temperature which leads to the transformation of the hydrologic cycle. Recent initiatives, including sustainable development goals, have been made to address these problems and offer solutions. However, the quantity and quality of freshwater systems and resources must be objectively and comprehensively understood and assessed at the scale of river basins to provide sufficient mitigation and resilience planning. Hydrologic modelling has been one the most suitable and efficient strategies for basin-scale assessment of freshwater dynamics to current and projected climate change and the focus has been on the application of traditional modelling framework which is tenable where data requirements are sufficient to couple hydrologic models with atmospheric data to account for climate change. The aforementioned strategy is a challenge in regions with inadequate ground-based observations necessary for climate and hydrologic modelling. The rarely available data in such regions may have repetitive gaps of missing data points with negative consequences including biased statistical representation of basin climatic features, ineffective model calibration and unreliable timing of peak flows which may amplify the uncertainties of the hydrologic dynamics leading to flawed depictions of watershed responses. Recently, integrated strategies are evolving that couple hydrologic models with climate data in water resource studies to account for uncertainties through the use of alternative data sources of many spatial climate data products from climate research centres to overcome the identified challenges. This research developed and applied a multi-criteria approach to examine the efficacy of gridded climate products using different performance metrics, a machine learning-based approach, Boruta random forest (BRF) to assess multiple GCM datasets required for hydro climatic studies and an integrated BRF-SWAT technique to define the relationship between the hydrologic variables and improve rainfall-runoff modelling in a data-sparse and climate sensitive watersheds. The developed model was applied to assess the projected green and blue water dynamics and sustainability in the Yobe-Komadugu basin of the greater Lake Chad, a watershed that is prone to extreme events (SPEI of flood and drought hazards). The results demonstrate that though the performance of the gridded data varies in space and time, multi-criteria assessment enhances the choice of a product with reduced uncertainty for climate modelling. The incorporation of the BRF approach in GCM evaluation indicates a consistent spatial and temporal representation of the climatological features with suitable mean correlation (R2 = 0.95), reduced mean annual precipitation bias of 0.69 mm/year and enhanced statistical trend and magnitude of the SPEI drought and flood hazards relative to identified and tested approaches from the literature. The integrated framework of the rainfall-runoff modelling strategy indicated that the hydrologic fluxes can be simulated fairly accurately with varying degrees of acceptability, irrespective of the watershed morphological properties, although there are significant trade-offs in model parameter sensitivity. The availability of satellite-based measurements of hydrologic fluxes and states, coupled with a machine learning feature selection and data refinement process has made integrated water balance modelling widely seen as a viable alternative for improving watershed hydrologic processes in data-sparse regions within acceptable uncertainty limits. Furthermore, the sub-watershed assessment of the projected changes in spatial and temporal green and blue water sustainability status has shown that the sub-basins will be ecologically fragile, and the identified freshwater geographic hotspots may be beyond restoration without adequate long-term river basin water resources plans. The modelling framework developed is, however, independent of the model and data type and can be applied to watersheds with similar modelling challenges. This study has provided a pathway or methods for managing and securing water resources information as a decision support tool to guarantee ongoing watershed monitoring and assessment of water security even in the face of increasingly unpredictable future circumstances in data-sparse watersheds that take into account uncertainty and chat a course for prospective risk assessment or the possibility and understanding that a certain effect brought on by climate-induced hazards would prevail in watershed freshwater sustainability. Therefore, it is essential to comprehend the constraints associated with forecasting changes in the water cycle to improve the climate and hydrologic modelling process, which is required to create effective strategies for adapting to climate change-related water resource hazards. Even in the face of severe uncertainty about the future, this will be essential in addressing concerns related to water security and management and promoting the climatic resilience of ecosystems and society

CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada

The performance of numerical, statistical, and data-driven diagnostic and predictive crop production modeling relies heavily on data quality for input and calibration or validation processes. This study presents a comprehensive database and the analytics used to consolidate it as a homogeneous, consistent, multidimensional genotype, phenotypic, and environmental database for maize phenotype modeling, diagnostics, and prediction. The data used are obtained from the Genomes to Fields (G2F) initiative, which provides multiyear genomic (G), environmental (E), and phenotypic (P) datasets that can be used to train and test crop growth models to understand the genotype by environment (GxE) interaction phenomenon. A particular advantage of the G2F database is its diverse set of maize genotype DNA sequences (G2F-G), phenotypic measurements (G2F-P), station-based environmental time series (mainly climatic data) observations collected during the maize-growing season (G2F-E), and metadata for each field trial (G2F-M) across the United States (US), the province of Ontario in Canada, and the state of Lower Saxony in Germany. The construction of this comprehensive climate and genomic database incorporates the analytics for data quality control (QC) and consistency control (CC) to consolidate the digital representation of geospatially distributed environmental and genomic data required for phenotype predictive analytics and modeling of the GxE interaction. The two-phase QC–CC preprocessing algorithm also includes a module to estimate environmental uncertainties. Generally, this data pipeline collects raw files, checks their formats, corrects data structures, and identifies and cures or imputes missing data. This pipeline uses machine-learning techniques to fill the environmental time series gaps, quantifies the uncertainty introduced by using other data sources for gap imputation in G2F-E, discards the missing values in G2F-P, and removes rare variants in G2F-G. Finally, an integrated and enhanced multidimensional database was generated. The analytics for improving the G2F database and the improved database called Climate for OMICS (CLIM4OMICS) follow findability, accessibility, interoperability, and reusability (FAIR) principles, and all data and codes are available at https://doi.org/10.5281/zenodo.8002909 (Aslam et al., 2023a) and https://doi.org/10.5281/zenodo.8161662 (Aslam et al., 2023b), respectively.</p

Regional groundwater levels in crystalline aquifers: structural domains, groundwater level monitoring, and factors controlling the response time and variability

This thesis aims to determine the degree to which fracture networks control the response time and fluctuation of groundwater levels in regional crystalline aquifers in comparison to topography, sediment deposits, precipitation and snowmelt. In this respect, the compartmentalization of the crystalline aquifer into structural domains is necessary, in order to take into account the heterogeneity of the crystalline aquifer in relation to the different fracture networks existing in the rock mass. Field investigations were conducted in the Lanaudiere region, Quebec, Canada, where the underlying crystalline rock outcrops in several locations, allowing access to outcrops for fracture sampling. In addition, four unequipped boreholes drilled into the crystalline rock were available for fracture sampling. Typically, fracture sampling involves the collection of multiple fracture samples, which involve numerous fracture clusters. Grouping fracture samples into structural domains is generally useful for geologists, hydrogeologists, and geomechanicians as a region of fractured rocks is subdivided into sub-regions with similar behavior in terms of their hydromechanical properties. One of the commonly used methods to group fracture samples into structural domains is Mahtab and Yegulalp's method, considering the orientation of fracture clusters and ignoring several fracture parameters, such as fracture spacing, aperture, and persistence, that are important for fluid circulation in the rock mass. In this thesis, we proposed a new cluster-based similarity method that considers cluster orientation as well as the aperture, persistence and spacing. In addition, a method for compartmentalizing a given study area into structural domains using Voronoi diagrams has also been proposed. The proposed method is more suitable than the previous method for applications in hydrogeology and rock mechanics, especially for regional studies of fluid flow in the rock mass. The study of response time and variability of groundwater levels requires a groundwater level monitoring network. The inclusion of private boreholes in these monitoring networks can provide a cost-effective means of obtaining a larger data set; however, the use of these boreholes is limited by the fact that frequent pumping, in these boreholes, generates outliers in the recorded time series. In this thesis, a slope criterion is applied to identify and remove outliers from groundwater level time series from exploited private boreholes. Nevertheless, the removal of outliers creates a missing value problem, which biases the subsequent time series analysis. Thus, 14 imputation methods were used to replace missing values. The proposed approach is applied to groundwater level time series from a monitoring network of 20 boreholes in the Lanaudiere region, Quebec, Canada. The slope criterion is shown to be very effective in identifying outliers in exploited private boreholes. Among the characteristics of the missing value pattern, the gap size and gap position in the time series are the most important parameters that affect the performance of the imputation methods. Among the imputation methods tested, linear and Stineman interpolations, and Kalman filtering were the most effective. This thesis demonstrates that privately operated boreholes can be used for groundwater monitoring by removing outliers and imputing missing values. At local and regional scales, groundwater level is controlled by several factors. The most commonly studied factors are climatic, geologic and geomorphologic controls on groundwater level variability and response time, and in many cases only one controlling factor is considered in the analysis. However, many other factors can affect groundwater level variability and response time, such as the sediment deposit properties and fracture network characteristics in crystalline aquifers. In this study, a more inclusive approach is used to consider climatic, geomorphological, and fracture network parameters as potential controlling factors. A total of 18 parameters were analyzed for interrelationships as each controlling factor is described by several parameters. The study analyzed a two-year record of groundwater levels in 20 boreholes, drilled into the crystalline rock of the Canadian Shield in the Lanaudière region, Québec, Canada. Factors associated to geomorpgology and fracture network are related to groundwater level variability and its response time. Of the various parameters analyzed in each control factor, sediment thickness and local slope of the geomorphological factor, as well as average persistence and equivalent hydraulic conductivity of the fracture network factor, are most closely related to groundwater level variability and response time. However, further studies are needed to elucidate the physical processes behind certain interrelationships between fracture network parameters and groundwater level variability parameters. Cette thèse a pour but de déterminer le degré auquel les réseaux de fractures contrôlent le temps de réponse et la fluctuation du niveau des eaux souterraines dans les aquifères cristallins régionaux par rapport à la topographie, aux dépôts de sédiments, aux précipitations et à la fonte des neiges. À cet égard, la compartimentation de l'aquifère cristallin en domaines structuraux est nécessaire, afin de prendre en compte l'hétérogénéité de l'aquifère cristallin par rapport aux différents réseaux de fractures existants dans le massif rocheux. Des investigations de terrain ont été menées dans la région de Lanaudière, Québec, Canada, où la roche cristalline sous-jacente affleure à plusieurs endroits, permettant un accès aux affleurements pour l'échantillonnage des fractures. De plus, quatre forages non équipés, forés dans la roche cristalline, étaient disponibles pour l'échantillonnage des fractures. Habituellement, l'échantillonnage de fractures comprend la collecte de plusieurs échantillons de fractures, qui impliquent de nombreux groupes de fractures. Le regroupement des échantillons de fractures en domaines structuraux est généralement utile pour les géologues, les hydrogéologues et les géomécaniciens dans la mesure où une région de roches fracturées est subdivisée en sous-régions ayant un comportement similaire en termes de propriétés hydromécaniques. L'une des méthodes couramment utilisées pour regrouper les échantillons de fractures en domaines structuraux est celle de Mahtab and Yegulalp, considérant l'orientation des clusters de fractures et ignorant plusieurs paramètres de fractures, tels que l'espacement, l'ouverture et la persistance des fractures, qui sont importants pour la circulation des fluides dans le massif rocheux. Dans cette thèse, nous avons proposé une nouvelle méthode de similarité basée sur les clusters qui considère l'orientation des clusters ainsi que l'ouverture, la persistance et l'espacement des clusters. En outre, une méthode pour la compartimentation d'une zone d'étude donnée en domaines structuraux au moyen de diagrammes de Voronoï a également été proposée. La méthode proposée est plus adaptée que la méthode précédente pour des applications en hydrogéologie et en mécanique des roches, notamment pour les études régionales de la circulation des fluides dans la masse rocheuse. L'étude du temps de réponse et de la variabilité du niveau des eaux souterraines nécessite un réseau de surveillance du niveau des eaux souterraines. L'inclusion de forages privés dans ces réseaux de surveillance peut fournir un moyen peu coûteux d'obtenir un ensemble plus large de données ; cependant, l'utilisation de ces forages est limitée par le fait que le pompage fréquent de ces forages génère des valeurs aberrantes dans les séries temporelles enregistrées. Dans cette thèse, un critère de pente est appliqué pour identifier et éliminer les valeurs aberrantes des séries temporelles du niveau des eaux souterraines provenant de forages privés exploités. Néanmoins, l'élimination des valeurs aberrantes crée un problème de valeurs manquantes, ce qui biaise l'analyse ultérieure des séries temporelles. Ainsi, 14 méthodes d'imputation ont été utilisées pour remplacer les valeurs manquantes. L'approche proposée est appliquée aux séries temporelles du niveau des eaux souterraines provenant d'un réseau de surveillance de 20 forages dans la région de Lanaudière, Québec, Canada. Le critère de pente s'avère très efficace pour identifier les valeurs aberrantes dans les forages privés exploités. Parmi les caractéristiques du modèle de valeurs manquantes, la taille et la position des lacunes dans la série temporelle sont les paramètres les plus importants qui affectent les performances des méthodes d'imputation. Parmi les méthodes d'imputation testées, les interpolations linéaires et de Stineman, ainsi que le filtrage de Kalman ont été les plus efficaces. La présente thèse démontre que les forages privés exploités peuvent être utilisés pour la surveillance des eaux souterraines en éliminant les valeurs aberrantes et en imputant les valeurs manquantes. À l'échelle locale et régionale, le niveau des eaux souterraines est contrôlé par plusieurs facteurs. Les facteurs les plus couramment étudiés sont les contrôles climatiques, géologiques et géomorphologiques sur la variabilité du niveau des eaux souterraines et le temps de réponse, et dans de nombreux cas, un seul facteur de contrôle est pris en compte dans l'analyse. Cependant, de nombreux autres facteurs peuvent affecter la variabilité du niveau des eaux souterraines et le temps de réponse, tels que les propriétés des dépôts de sédiments et les caractéristiques du réseau de fractures dans les aquifères cristallins. Dans cette étude, une approche plus globale est utilisée pour considérer les paramètres climatiques, géomorphologiques et du réseau de fractures comme des facteurs de contrôle potentiels. Au total, 18 paramètres ont été analysés pour déterminer les interrelations, sachant que chaque facteur de contrôle est décrit par plusieurs paramètres. L'étude a analysé un jeu de données de deux ans sur les niveaux d'eau souterraine dans 20 forages réalisés dans la roche cristalline du Bouclier canadien dans la région de Lanaudière, au Québec, Canada Les facteurs liés à la géomorphologie et au réseau de fractures sont liés à la variabilité du niveau des eaux souterraines et à son temps de réponse. Parmi les divers paramètres analysés dans chaque facteur de contrôle, l'épaisseur des sédiments et la pente locale du facteur géomorphologique, ainsi que la persistance moyenne et la conductivité hydraulique équivalente du facteur réseau de fractures, sont les plus étroitement liés à la variabilité du niveau des eaux souterraines et à son temps de réponse. Toutefois, des études complémentaires sont nécessaires pour élucider les processus physiques à l'origine de certaines interrelations entre les paramètres du réseau de fractures et les paramètres de variabilité du niveau des eaux souterraines

An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR) scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC) simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting and comparing forecasts against available historical data (1987–2011) for spring wheat (Triticum aestivum L.). The model was also validated for the 2012 growing season by comparing forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1–4% in mid-season and over-estimated by 1% at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space

Changing Deserts

Deserts – vast, empty places where time appears to stand still. The very word conjures images of endless seas of sand, blistering heat and a virtual absence of life. However, deserts encompass a large variety of landscapes and life beyond our stereotypes. As well as magnificent Saharan dunes under blazing sun, the desert concept encompasses the intensely cold winters of the Gobi, the snow-covered expanse of Antarctica and the rock-strewn drylands of Pakistan. Deserts are environments in perpetual flux and home to peoples as diverse as their surroundings, peoples who grapple with a broad spectrum of cultural, political and environmental issues as they wrest livelihoods from marginal lands. The cultures, environments and histories of deserts, while fundamentally entangled, are rarely studied as part of a network. To bring different disciplines together, the 1st Oxford Interdisciplinary Deserts Conference in March 2010 brought together a wide range of researchers from backgrounds as varied as physics, history, archaeology anthropology, geology and geography. This volume draws on the diversity of papers presented to give an overview of current research in deserts and drylands. Readers are invited to explore the wide range of desert environments and peoples and the ever-evolving challenges they face