The Sahel region located in Western Africa is well known for its high rainfall variability. Severe and recurring droughts have plagued the region during the last three decades of the 20th century, while heavy precipitation events (with return periods of up to 1,200 years) were reported between 2007 and 2014. Vulnerability to extreme events is partly due to the fact that people are not prepared to cope with them. It would be of great benefit to farmers if information about the magnitudes of precipitation and streamflow in the upcoming rainy season were available a few months before; they could then switch to more adapted crops and farm management systems if required. Such information would also be useful for other sectors of the economy, such as hydropower production, domestic/industrial water consumption, fishing and navigation. A logical solution to the above problem would be seasonal rainfall and streamflow forecasting, which would allow to generate knowledge about the upcoming rainy season based on information available before it's beginning. The research in this thesis sought to improve seasonal rainfall and streamflow forecasting in the Sahel by developing statistical rainfall and streamflow seasonal forecasting models. Sea surface temperature (SST) were used as pools of predictor. The developed method allowed for a systematic search of the best period to calculate the predictor before it was used to predict average rainfall or streamflow over the upcoming rainy season. Eight statistical models consisted of various statistical methods including linear and polynomial regressions were developed in this study. Two main approaches for seasonal streamflow forecasting were developed here: 1) A two steps streamflow forecasting approach (called the indirect method) which first linked the average SST over a period prior to the date of forecast to average rainfall amount in the upcoming rainy season using the eight statistical models, then linked the rainfall amount to streamflow using a rainfall-runoff model (Soil and Water Assessment Tool (SWAT)). In this approach, the forecasted rainfall was disaggregated to daily time step using a simple approach (the fragment method) before being fed into SWAT. 2) A one step streamflow forecasting approach (called as the direct method) which linked the average SST over a period prior to the date of forecast to the average streamflow in the upcoming rainy season using the eight statistical models. To decrease the uncertainty due to model selection, Bayesian Model Averaging (BMA) was also applied. This method is able to explore the possibility of combining all available potential predictors (instead of selecting one based on an arbitrary criterion). The BMA is also capability to produce the probability density of the forecast which allows end-users to visualize the density of expected value and assess the level of uncertainty of the generated forecast. Finally, the economic value of forecast system was estimated using a simple economic approach (the cost/loss ratio method). Each developed method was evaluated using three well known model efficiency criteria: the Nash-Sutcliffe coefficient (Ef), the coefficient of determination (R2) and the Hit score (H). The proposed models showed equivalent or better rainfall forecasting skills than most research conducted in the Sahel region. The linear model driven by the Pacific SST produced the best rainfall forecasts (Ef = 0.82, R2 = 0.83, and H = 82%) at a lead time of up to 12 months. The rainfall forecasting model based on polynomial regression and forced by the Atlantic ocean SST can be used using a lead time of up to 5 months and had a slightly lower performance (Ef = 0.80, R2 = 0.81, and H = 82%). Despite the fact that the natural relationship between rainfall and SST is nonlinear, this study found that good results can be achieved using linear models. For streamflow forecasting, the direct method using polynomial regression performed slightly better than the indirect method (Ef = 0.74, R2 = 0.76, and H = 84% for the direct method; Ef = 0.70, R2 = 0.69, and H = 77% for the indirect method). The direct method was driven by the Pacific SST and had five months lead time. The indirect method was driven by the Atlantic SST and had six months lead time. No significant difference was found in terms of performance between BMA and the linear regression models based on a single predictor for streamflow forecasting. However, BMA was able to provide a probabilistic forecast that accounts for model selection uncertainty, while the linear regression model had a longer lead time. The economic value of forecasts developed using the direct and indirect methods were estimated using the cost/loss ratio method. It was found that the direct method had a better value than the indirect method. The value of the forecast declined with higher return periods for all methods. Results also showed that for the particular watershed under investigation, the direct method provided a better information for flood protection. This research has demonstrated the possibility of decent seasonal streamflow forecasting in the Sirba watershed, using the tropical Pacific and Atlantic SSTs as predictors.The findings of this study can be used to improve the performance of seasonal streamflow forecasting in the Sahel. A package implementing the statistical models developed in this study was developed so that end users can apply them for seasonal rainfall or streamflow forecasting in any region they are interested in, and using any predictor they may want to try.

Numerical models often fail to accurately simulate and forecast a hydrological state in operation due to its inherent uncertainties. Data Assimilation (DA) is a promising technology that uses real-time observations to modify a model's parameters and internal variables to make it more representative of the actual state of the system it describes. In this thesis, hydrological DA is first reviewed from the perspective of its objective, scope, applications and the challenges it faces. Special attention is then given to nonlinear Kalman filters such as the Extended Kalman Filter (EKF). Based on a review of the existing studies, it is found that the potential of EKF has not been fully exploited. The Soil and Water Assessment Tool (SWAT) is a semi-distributed rainfall-runoff model that is widely used in agricultural water management and flood forecasting. However, studies of hydrological DA that are based on distributed models are relatively rare because hydrological DA is still in its infancy, with many issues to be resolved, and linear statistical models and lumped rainfall-runoff models are often used for the sake of simplicity. This study aims to fill this gap by assimilating streamflow and surface soil moisture observations into the SWAT model to improve its state simulation and forecasting capability. Unless specifically defined, all ‘forecasts’ in Italic font are based on the assumption of a perfect knowledge of the meteorological forecast. EKF is chosen as the DA method for its solid theoretical basis and parsimonious implementation procedures. Given the large number of parameters and storage variables in SWAT, only the watershed scale variables are included in the state vector, and the Hydrological Response Unit (HRU) scale variables are updated with the a posteriori/a priori ratio of their watershed scale counterparts. The Jacobian matrix is calculated numerically by perturbing the state variables. Two case studies are carried out with real observation data in order to verify the effectiveness of EKF assimilation. The upstream section of the Senegal River (above Bakel station) in western Africa is chosen for the streamflow assimilation, and the USDA ARS Little Washita experimental watershed is chosen to examine surface soil moisture assimilation. In the case of streamflow assimilation, a spinoff study is conducted to compare EKF state-parameter assimilation with a linear autoregressive (AR) output assimilation to improve SWAT’s flood forecasting capability. The influence of precipitation forecast uncertainty on the effectiveness of EKF assimilation is discussed in the context of surface soil moisture assimilation. In streamflow assimilation, EKF was found to be effective mostly in the wet season due to the weak connection between runoff, soil moisture and the curve number (CN2) in dry seasons. Both soil moisture and CN2 were significantly updated in the wet season despite having opposite update patterns. The flood forecast is moderately improved for up to seven days, especially in the flood period by applying the EKF subsequent open loop (EKFsOL) scheme. The forecast is further improved with a newly designed quasi-error update scheme. Comparison between EKF and AR output assimilation in flood forecasting reveals that while both methods can improve forecast accuracy, their performance is influenced by the hydrological regime of the particular year. EKF outperformed the AR model in dry years, while AR outperformed the EKF in wet years. Compared to AR, EKF is more robust and less sensitive to the length of the forecast lead time. A combined EKF-AR method provides satisfying results in both dry and wet years. The assimilation of surface soil moisture is proved effective in improving the full profile soil moisture and streamflow estimate. The setting of state and observation vector has a great impact on the assimilation results. The state vector with streamflow and all-layer soil moisture outperforms other, more complicated state vectors, including those augmented with intermediate variables and model parameters. The joint assimilation of surface soil moisture and streamflow observation provides a much better estimate of soil moisture compared to assimilating the streamflow only. The updated SWAT model is sufficiently robust to issue improved forecasts of soil moisture and streamflow after the assimilation is ‘unplugged’. The error quantification is found to be critical to the performance of EKF assimilation. Nevertheless, the application of an adaptive EKF shows no advantages over using the trial and error method in determining time-invariant model errors. The robustness of EKF assimilation is further verified by explicitly perturbing the precipitation ‘forecast’ in the EKF subsequent forecasts. The open loop model without previous EKF update is more vulnerable to erroneous precipitation estimates. Compared to streamflow forecasting, soil moisture forecasting is found to be more resilient to erroneous precipitation input.

Dissertation Business process improvement possibilities in internet shop introduce reader with improvement possibilities in small e-shops with limited financial competence. Object of this work is determination conception process, finding suitable methods for process improvement in enterprise and on the basis of obtained information create process model chosen e-shop and usage some method for process improvement. At the beginning is reader introduced with concept e-shop and e-business. Next part is explained concept and sense of process access in companies operating in nowadays. Next part is about process improvement. First are explained concepts Business process improvement and Business process reengineering than are introduced methods suitable for process improvement. Methods are chosen from different areas, so is good to see the variety of ways that heads to process improvement. Next part is about MIPIM methodology. All steps are explained and extended with suitable methods and tools for each step. Next chapter introduces SkateMORE e-shop. Using SWAT analysis and strategic goals show us possible ways of process modification. Last chapter put deals with process improvement in practically using MIPIM methodology and chosen method mentioned in foregoing chapters. Dissertation ends with rating design of solution implementation. Implementation and rating is not a part of dissertation

Around the world, many forested areas have been and continue to be cleared for expanding agriculture. Canada’s remaining forested lands account for around 9% of the world’s forest cover. Although only a fraction is lost to deforestation annually (0.02%, 2013), Statistics Canada reports that conversion to agriculture is the most significant driver of forest loss. As climate changes and agricultural demand expands, this trend is expected to continue, and ecosystems will continue to be impacted by resulting habitat loss and hydrological changes that can impact infrastructure and communities. Additionally, changes to sediment and nutrient loadings can harm ecosystems and affect the downstream usability of freshwater supplies. The impact of increased sediment and nutrient concentrations in freshwater systems has been extensively documented in the literature. In some extreme cases, it can lead to anoxic ‘dead zones’ in riverine, lacustrine, and marine habitats. Many river systems in Canada have shown elevated nutrient levels in recent years, often tied to the expansion of agricultural land use and destruction of natural forests to increasing nutrient levels in downstream rivers, lakes, and oceans. This study applies numerical modelling to quantify the influence of forest loss, agricultural expansion and the application of best management practices (BMPs) on water quality and quantity in the South Nation Watershed in eastern Ontario, Canada. The land use in the watershed is mainly agricultural (over 60%) with forest (27%) that is unevenly distributed in the basin. Aerial photography surveys from 2008 and 2014 show a steady decline in forest cover. Recent water quality monitoring has shown nutrient concentrations at or above Canadian water quality standards in many parts of the basin. The Soil and Water Assessment Tool (SWAT) was used to model the watershed because of its capacity to simulate comprehensive land management scenarios and assess their impact on a variety of water quantity and parameters quickly and effectively. The work was performed in four steps: 1. Recent land use configurations (2008-2014) in the watershed were acquired, and simplified land use projections based on the direct substitution of cropland for forest land were developed. 2. A numerical model was calibrated and validated for the initial land use scenario. 3. These land use scenarios, as well as more hypothetical scenarios representing more extensive deforestation and reforestation, were used as the basis for hydrological modelling using 31 years of real-world meteorological observations. 4. Idealized vegetated filter strips (VFSs) and grassed waterways (GWWs) were added to the cropped land packages to study the potential of these practices to contribute to the management of water quality. Analysis of the 33 output datasets derived from simulations of the suite of land use scenarios with and without VFSs and GWWs leads to several conclusions, while also raising some questions. Generally, forests significantly reduce sediment, nitrate and phosphorus outputs to streams as well as slightly reducing water yield compared to cropped areas due to an increase in surface runoff, groundwater and lateral flow combined with the absence of tile drainage. Across subbasins, this translates to significant reductions in sediment, nitrate and total phosphorus loadings entering the river reaches and a slight increase in water yield. At the basin outlet near Plantagenet, Ontario, streamflow and sediment loading show to have little sensitivity to changes in forest and crop cover, while increased forest cover leads to significantly reduced nutrient loadings, particularly in late spring and early winter. It is clear from this work that continued deforestation will continue to drive further nutrient enrichment in the South Nation River, while VFSs seems to have a significant potential for offsetting some of this enrichment. Streamflow and sediment loadings, however, are not significantly impacted by foreseeable deforestation. The influence of land use change and BMPs was much more significant in the runoff than in exports from the basin, suggesting there would be value in further examination of water quality and quantity at a higher spatial density to expand on assumptions of in-stream processes made here.

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This study provides a long-term understanding of the impact of climate varia-bility and land use on seasonal water balance conditions for sustainable agricul-ture development, hydropower generation and ecosystem stability in the Lower Rufiji Basin. The severity of soil drought, extreme flooding and salinity intru-sion in the lower Rufiji floodplains are currently increasing smallholder poverty and enhance the sensitivity on the natural wetlands for shifting farming and livestock pastures. The CoupModel and SWAT hydrological model were ap-plied to assess and compare the impact of climate variability on the water bal-ance. The monthly river discharge was used for calibrating and validating the runoff at the Stiegler's Gorge. The simulated results for water balance compo-nents at Stiegler's Gorge showed 55% of accumulated precipitation is lost through evapotranspiration and 42 % is river runoffs for downstream agricul-ture and ecosystem services. The evaluation of the models simulation perfor-mance and posterior distribution of parameter behavioral value indicates the (GLUE) calibration method in the CoupModel agreed satisfactory with the Bayesian calibration (BC). The minimal variance in the Bayesian Calibration posterior parameter distribution was observed in the parameter for regulating water uptake from (CritThresholDry) and soil moisture availability for soil evaporation(PsiRs_ip). The SWAT simulation showed that south of the central floodplains has high risk of soil drought. The overall assessment implies that drought and river runoff dynamics in the LRB is affected by upstream land use activities. The strategies for building smallholder resilience towards climate change and land use impact requires collective and coordinated water manage-ment actions powered by individual, institutional, financial and technological adaptation.

For decades, stress has been scientifically studied and found to have effects on the law enforcement community. Furthermore, scholars have thoroughly studied the correlation between stress and the law enforcement occupation which has been proven to affect their well-being. Although there is currently ample literature on stress and police officers, to date there has been little research on factors associated with stress and SWAT police officers. Using Lazarus and Folkman's cognitive theory of stress and coping as the foundation, the purpose of this phenomenological study was to explore how SWAT police officers cope with stress while on duty and off duty and the factors that cause them stress. Participants included 5 retired police officers who were members of a SWAT team. Moustakas' framework design of phenomenological study assisted in identifying common themes that emerged from participant interviews. Study findings indicate that the primary stressor to SWAT officers was responding to high-risk missions or operations and that law enforcement agencies generally fail to provide the resources needed to cope with stress. In addition, the main coping resources used by tactical officers were self-initiated activities such as exercise, spending time with family, and hobbies (e.g., hunting, fishing, camping, hiking, and sports entertainment). The results of this study encourage positive social change by advancing recommendations to law enforcement leadership to develop coping resources for tactical officers that are specific to their unique needs. The study also increases awareness and knowledge of the coping resources that SWAT officers need and advocating for new programs and trainings aimed at reducing stress for them, which may prevent officer burnout and improve public safety response.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第19409号 / 工博第4125号 / 新制||工||1636(附属図書館) / 32434 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 寶 馨, 教授 立川 康人, 准教授 佐山 敬洋 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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