Agricultural production is highly sensitive to climate and weather perturbations. Maize is the main crop cultivated in South Africa and production is predominantly rain-fed. South Africa’s climate, especially rainfall, is extremely variable which influences the water available for agriculture and makes rain-fed cropping very risky. In the aim to reduce the uncertainty in the climate of the forthcoming season, this study investigates whether seasonal climate forecasts can be used to predict maize yields for South Africa with a usable level of skill. Maize yield, under rain-fed conditions, is simulated for each of the magisterial districts in the primary maize producing region of South Africa for the period from 1979 to 1999. The ability of the CERES-Maize model to simulate South African maize yields is established by forcing the CERES-Maize model with observed weather data. The simulated maize yields obtained by forcing the CERES-Maize model with observed weather data set the target skill level for the simulation systems that incorporate Global Circulation Models (GCMs). Two GCMs produced the simulated fields for this study, they are the Conformal Cubic Atmospheric Model (CCAM) and the ECHAM4.5 model. CCAM ran a 5 and ECHAM4.5 a 6- member ensemble of simulations on horizontal grids of 2.1° x 2.1° and 2.8° x 2.8° respectively. Both models were forced with observed sea-surface temperatures for the period 1979 to 2003. The CERES-Maize model is forced with each ensemble member of the CCAM-simulated fields and with each ensemble member of the ECHAM4.5-simulated fields. The CERES-CCAM simulated maize yields and CERES-ECHAM4.5 simulated maize yields are combined to form a Multi-Model maize yield ensemble system. The simulated yields are verified against actual maize yields. The CERES-Maize model shows significant skill in simulating South Africa maize yields. CERES-Maize model simulations using the CCAM-simulated fields produced skill levels comparable to the target skill, while the CERES-ECHAM4.5 simulation system illustrated poor skill. The Multi-Model system presented here could therefore not outscore the skill of the best single-model simulation system (CERES-CCAM). Notwithstanding, the CERES-Maize model has the potential to be used in an operational environment to predict South African maize yields, provided that the GCM forecast fields used to force the model are adequately skilful. Such a yield prediction system does not currently exist in South Africa. / Dissertation (MSc)--University of Pretoria, 2009. / Geography, Geoinformatics and Meteorology / Unrestricted

More than 1100 debris flows were mobilized from shallow landslides during a rainstorm from 9 to 13 September 2013 in the Colorado Front Range, with the vast majority initiating on sparsely vegetated, south facing terrain. To investigate the physical processes responsible for the observed aspect control, we made measurements of soil properties on a densely forested north facing hillslope and a grassland-dominated south facing hillslope in the Colorado Front Range and performed numerical modeling of transient changes in soil pore water pressure throughout the rainstorm. Using the numerical model, we quantitatively assessed interactions among vegetation, rainfall interception, subsurface hydrology, and slope stability. Results suggest that apparent cohesion supplied by roots was responsible for the observed connection between debris flow initiation and slope aspect. Results suggest that future climate-driven modifications to forest structure could substantially influence landslide hazards throughout the Front Range and similar water-limited environments where vegetation communities may be more susceptible to small variations in climate.

The objective of this study was to identify environmental and social parameters associated with the incidence of American Tegumentary Leishmaniasis (ATL) in the municipality of Ubatuba, Sao Paulo State, Brazil, in 2003, which can supply information to subsidy the Control Programs for ATL. Ubatuba is one of the cities in the north coastal area of the State, where the climate can be characterized as tropical humid. With an extensive area of natural vegetation, Lutzomyia intermedia is the predominant transmitting phlebotominae species. It was selected as study sample 60 cases of ATL, notified at 2003, and to prepare data about some environmental aspects related to the housing and work conditions of each case were observed; through domiciliary visits and application of a form is also collected data related to social and conditions of the subject of the investigation and their knowledge degree about the illness and its relation with the environment. The meteorological conditions related to rain fall and temperature were analyzed comparing to the historical series of the past 9 years before investigation, from 1994 to 2001. Global Positioning System (GPS) images were used as analysis tool to study the spatial distribution of the ATL. Rain fall and temperature data were analyzed, month to month. The annual temperature averages, during the studied period, varied from 21,5 to 22,5C, between 1994 and 2001; higher temperatures, with month averages ranging from 24 to 27C, were observed during the Summer months, between December and March, and lower temperatures, 18 to 21 C, between June and September. The annul rainfall precipitation averages varied from 147,5mm to 267,2mm. The occurrence of ATL does not seem to be associated to the history of recent occupation, specific age group or type of work activity, nor to the rainfall variations. It is discussed the hypothesis of the contribution of the temperature for higher number of cases of ATL in 2003. / O objetivo deste estudo foi identificar parâmetros ambientais e sociais e associá-los com a incidência de Leishmaniose Tegumentar Americana (LTA), no município de Ubatuba, Estado de São Paulo, Brasil, entre os anos de 1994 e 2003, e fornecer informações que possam servir de subsídio ao programa de controle da LTA. Ubatuba está situado no litoral norte do Estado de São Paulo, onde o clima é caracterizado como tropical úmido, com uma extensa área de vegetação natural, sendo Lutzomyia intermedia a espécie predominante do flebotomíneo transmissor. Foi selecionada para o estudo uma amostra de 60 casos de LTA, notificados no ano de 2003, e levantados os dados relativos a aspectos ambientais, condições de moradias e ocupação de cada caso que foi notificado. Por meio de visitas domiciliares e aplicação de um formulário, foram também coletados dados sobre condições sociais dos sujeitos da pesquisa, grau de conhecimento sobre a doença e sua relação com meio ambiente. As variações climáticas de pluviometria e temperatura foram analisadas mês a mês e comparadas aos registrados na série histórica dos 09 anos anteriores à pesquisa, As imagens de geoposicionamento, foram utilizadas como ferramenta de estudo da distribuição da LTA nos bairros de ocorrência. A variação das médias anuais de temperatura, durante o período estudado, manteve-se entre 21,5 e 22,5C, entre 1994 e 2001, enquanto que em 2003, elevou-se a 22,9C e em 2003 retorna a 22,5C. As maiores médias mensais de temperatura, 24 a 27C, foram observadas durante os meses de verão, entre dezembro e março e as menores temperaturas, 18 a 21C entre os meses de junho e setembro. As médias anuais de pluviometria variaram de 147,5mm a 267,2mm. A ocorrência de LTA não parece estar associada a recente ocupação e a grupo específico, seja quanto à faixa etária ou ao tipo de atividade laborativa, nem a variações anuais de pluviometria. Discute-se a hipótese da contribuição da temperatura para o aumento de casos de LTA em 2003.

CASCADE 2001 is a multi-basin flood routing program used in areas of flat terrain. CASCADE was used for different situational elements including the Florida Keys, Broward County, and Pensacola. The goal for this screening tool was to create flood inundation watershed mapping for the Florida Division of Emergency Management (FDEM). After showing the risks of flooding that could occur in Florida, the thought of how useful CASCADE can be in other environmental conditions. The Rocky Mountains were selected to show the effect of flood inundation that can be mirrored in an opposite condition from prior experimentation. We chose to test this program in an area with mountainous terrain like the region of Grand Lake, Colorado. Rainfall, in collaboration with groundwater tables, ground soil storage and topography have the most effect on the CASCADE modeling program. Effects that were not used in the Florida models but added for Grand Lake included snowmelt. Snowmelt in the Rocky Mountains affects the flow of the Colorado River causing excess discharge that flows throughout the valleys and into Shadow Mountain Lake. WINSRM was a recommended model that could be used to simulate snowmelt during different months of Colorado’s spring season. The effects of snowmelt and rainfall flooding can be compared in relation to each other. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

More than a billion dollars are spent annually on stream restoration in the United States (Bernhardt et al., 2005), but the science remains immature. A promising technique for estimating a single or range of design discharges is the generalization of a parsimonious conceptual continuous simulation model. In this study the Probability Distributed Model (PDM), was generalized for the Maryland and Virginia Piedmont. Two hundred and sixty years of daily average flow data from fifteen watersheds were used to calibrate PDM. Because the application of the study is to stream restoration, the model was calibrated to discharges greater than two times baseflow and less than flows with a return period of ten years. The hydrologic calibration parameters were related to watershed characteristics through regression analysis, and these equations were used to calculate regional model parameters based on watershed characteristics for a single "ungaged" independent evaluation watershed in the region. Simulated flow was compared to observed flow; the model simulated discharges of lower return periods moderately well (e.g., within 13% of observed for a flow with a five year return period). These results indicate this technique may be useful for stream restoration and wetland design. / Master of Science

Limited research has been conducted and uncertainty exists regarding sugarcane response to water stress during different development phases. This information is necessary to optimize the allocation of limited irrigation water for sugarcane production. The objective of this study was to understand and quantify the response of crop water use (CWU), canopy development, stalk elongation, biomass accumulation and partitioning, and sugarcane yield to mild water stress, imposed through deficit drip irrigation, during different development phases. A field experiment consisting of a plant and first ratoon crop of cultivar N49 was conducted near Komatipoort. For the three water stress treatments, available soil water (ASW) was maintained between 30 and 60% of capacity during the tillering phase (TP), stalk elongation phase (SEP) and through both phases. ASW was maintained above 60% of capacity in the well-watered control and during periods when stress was not intended. Rainfall prevented water stress from developing in the TP of the plant crop. In the ratoon crop, 72% less irrigation was applied in the TP, resulting in 50 days of stress (ASW<50%). This did not affect stalk population but reduced CWU by 13%, shortened stalks by 21% and affected the canopy by reducing green leaf number (GLN) and green leaf area index (GLAI). Relieving the stress during SEP allowed the crop to re-establish its canopy, capture adequate photosynthetically active radiation (PAR) and restore rates of photo-assimilation (as suggested by CWU) and stalk elongation to support rapid biomass production. This restoration of plant processes allowed the ratoon crop to attain a cane and stalk dry biomass (SDM) yield that was only 9 and 11% lower (statistically insignificant), respectively, than the well-watered control at lodging (crop age of 286 days). During the SEP of the plant and ratoon crop, 42 and 85% less irrigation was applied, resulting in the crops experiencing 74 and 39 days of stress and using 7 and 8% less water, respectively. This did not affect stalk population or the crop canopy, but reduced stalk height by about 6 and 14% in the plant and ratoon crops, respectively. In both crops, shorter stalks and a negatively affected CWU which reduced photo-assimilate production, reduced cane yield by 14 and 10% (statically insignificant) and SDM yield by 15 and 5% (statistically insignificant), in the plant and ratoon crops respectively. © University of Pretoria iv Deficit irrigation throughout the TP and SEP of the ratoon crop reduced irrigation amount by 74%, resulting in 110 days of stress and reducing CWU by 16% and stalk height by 14%. PAR capture was reduced through reduced GLAI. This resulted in a significant reduction of 15% in cane yield. SDM yield was reduced by 17%, although this was not statistically significant. Stalk sucrose content was not influenced by deficit irrigation but was rather dependent on the duration of the drying-off period prior to harvest. Sucrose yields were therefore largely determined by SDM. Results suggest that the soil water potential (SWP) measured at 0.25 and 0.40 m depths, halfway between drip emitters within a plant or ratoon crop, can drop to about -40 kPa before irrigation is applied, without sacrificing cane or sucrose yield. Lastly, a ratoon crop can rapidly recover from stress during the TP, provided that the SWP during SEP is maintained above -40 kPa. / Dissertation (MSc Agric)--University of Pretoria, 2013. / gm2014 / Plant Production and Soil Science / unrestricted

>Magister Scientiae - MSc / This research objectively investigates the e ectiveness of machine learning (ML) tools towards predicting several geo-physical parameters. This is based on a large number of studies that have reported high levels of prediction success using ML in the eld. Therefore, several widely used ML tools coupled with a number of di erent feature sets are used to predict six geophysical parameters namely rainfall, groundwater, evapora- tion, humidity, temperature, and wind. The results of the research indicate that: a) a large number of related studies in the eld are prone to speci c pitfalls that lead to over-estimated results in favour of ML tools; b) the use of gaussian mixture models as global features can provide a higher accuracy compared to other local feature sets; c) ML never outperform simple statistically-based estimators on highly-seasonal parame- ters, and providing error bars is key to objectively evaluating the relative performance of the ML tools used; and d) ML tools can be e ective for parameters that are slow- changing such as groundwater.

Weather stations provide key information related to soil moisture and have been used by farmers to decide various field operations. We first evaluated the discrepancies in soil moisture between a weather stations and nearby field; due to soil texture, crop residue cover, crop type, growth stage and duration of temporal dependency to recent rainfall and evaporation rates using regression analysis. The regression analysis showed strong relationship between soil moisture at the weather station and the nearby field at the late vegetative and early reproductive stages. The correlation thereafter declines at later growth stages for corn and wheat. We can adduce that the regression coefficient of soil moisture with four-day cumulative rainfall slightly increased with an increase in the crop residue resulting in a low root mean square error (RMSE) value. We then investigated the effectiveness of machine learning techniques such as random forest regression (RFR), boosted regression trees (BRT), support vector regression, and artificial neural network to predict soil moisture in nearby fields based on RMSE of a 30% validation dataset and to determine the relative importance of predictor variables. The RFR and BRT performed best over other machine learning algorithms based on the lower RMSE values of 0.045 and 0.048 m3 m-3, respectively. The Classification and Regression Trees (CART), RFR and BRT models showed soil moisture at nearby weather stations had the highest relative influence for moisture prediction, followed by the four-day cumulative rainfall and Potential Evapotranspiration (PET), and subsequently followed by bulk density and Saturated Hydraulic Conductivity (Ksat). We then evaluated the integration of weather station data, RFR machine learning, and remotely sensed satellite imagery to predict soil moisture in nearby fields. Soil moisture predicted with an RFR algorithm using OPtical TRApezoidal Model (OPTRAM) moisture values, rainfall, standardized precipitation index (SPI) and percent clay showed high goodness of fit (r2=0.69) and low RMSE (0.053 m3 m-3). This research shows that the integration of weather station data, machine learning, and remote sensing tools can be used to effectively predict soil moisture in the Red River Valley of the North among a large diversity of cropping systems.

Indiana University-Purdue University Indianapolis (IUPUI) / Drylands cover 40% of the earth’s terrestrial surface supporting over 2 billion people, the majority of whom reside in developing nations characterised by high population growth rates. This imposes pressure on the already limited water resources and in some dryland regions such as southern Africa, the origins and dynamics of rainfall are not well understood. Research has also tended to focus on factors limiting (e.g., rainfall) than sustaining productivity in drylands. However, non-rainfall water (NRW) e.g., fog and dew can supplement and/or exceed rainfall in these environments and could potentially be exploited as potable water resources. Much remains unknown in terms of NRW formation mechanisms, origins, evolution, potability and potential impact of global climate change on these NRW dependent ecosystems. Using Namibia as a proxy for drylands and developing nations, this dissertation applies stable isotopes of water (δ2H, δ18O, δ17O and d-excess), cokriging and trajectory analysis methods to understand ecohydrological processes. Results suggest that locally generated NRW may be a regular occurrence even in coastal areas such as the Namib Desert, and that what may appear as a single fog event may consist of different fog types co-occurring. These results are important because NRW responses to global climate change is dependent on the source, groundwater vs. ocean, and being able to distinguish the two will allow for more accurate modelling. I also demonstrate, that fog and dew formation are controlled by different fractionation processes, paving the way for plant water use strategy studies and modelling responses to global climate change. The study also suggests that current NRW harvesting technologies could be improved and that the potability of this water could raise some public health concerns related to trace metal and biological contamination. At the same time, the dissertation concludes that global precipitation isoscapes do not capture local isotope variations in Namibia, suggesting caution when applied to drylands and developing nations. Finally, the dissertation also reports for the first time, δ17O precipitation results for Namibia, novel isotope methods to differentiate synoptic from local droughts and suggests non-negligible moisture contributions from the Atlantic Ocean due to a possible sub-tropical Atlantic Ocean dipole.

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