Flood Prediction and Mitigation in Data-Sparse Environments

In the last three decades many sophisticated tools have been developed that can accurately predict the dynamics of flooding. However, due to the paucity of adequate infrastructure, this technological advancement did not benefit ungauged flood-prone regions in the developing countries in a major way. The overall research theme of this dissertation is to explore the improvement in methodology that is essential for utilising recently developed flood prediction and management tools in the developing world, where ideal model inputs and validation datasets do not exist. This research addresses important issues related to undertaking inundation modelling at different scales, particularly in data-sparse environments. The results indicate that in order to predict dynamics of high magnitude stream flow in data-sparse regions, special attention is required on the choice of the model in relation to the available data and hydraulic characteristics of the event. Adaptations are necessary to create inputs for the models that have been primarily designed for areas with better availability of data. Freely available geospatial information of moderate resolution can often meet the minimum data requirements of hydrological and hydrodynamic models if they are supplemented carefully with limited surveyed/measured information. This thesis also explores the issue of flood mitigation through rainfall-runoff modelling. The purpose of this investigation is to assess the impact of land-use changes at the sub-catchment scale on the overall downstream flood risk. A key component of this study is also quantifying predictive uncertainty in hydrodynamic models based on the Generalised Likelihood Uncertainty Estimation (GLUE) framework. Detailed uncertainty assessment of the model outputs indicates that, in spite of using sparse inputs, the model outputs perform at reasonably low levels of uncertainty both spatially and temporally. These findings have the potential to encourage the flood managers and hydrologists in the developing world to use similar data sets for flood management

Simulations and predictions of mosquito populations in rural Africa using rainfall inputs from satellites and forecasts

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Page 102 blank. Cataloged from PDF version of thesis.Includes bibliographical references (p. 94-101).This thesis describes studies on the use of the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS) developed and tested against field data by Bomblies et al. (2008) in simulating and predicting the potential for malaria transmission in rural Africa. The first study examined the temporal resolution of rainfall input required by HYDREMATS. Simulations conducted over Banizoumbou village in Niger showed that for reasonably accurate simulation of mosquito populations, the model requires rainfall data with at least 1 hour resolution. The second study investigated whether HYDREMATS could be effectively forced by satellite based estimates of rainfall instead of ground based observations. The CPC Morphing technique (CMORPH) (Joyce et al., 2004) precipitation estimates distributed by NOAA are available at a 30-minute temporal resolution and 8 km spatial resolution. We compared mosquito populations simulated by HYDREMATS when the model is forced by adjusted CMORPH estimates and by ground observations. The results indicate that adjusted CMORPH rainfall estimates can be used with HYDREMATS to simulate the dynamics of mosquito populations and malaria transmission with accuracy similar to that obtained when using ground observations of rainfall. The third study tested the ability of HYDREMATS to make short term predictions about mosquito populations. A method was developed by which the rainfall forcing for HYDREMATS is constructed to suit a prediction mode. Observed rainfall is used up until the date of the prediction. The rainfall for the following two weeks (or four weeks) is assumed to be the seasonal mean for that period. HYDREMATS predictions using this method were not significantly different from simulations using observed data.This thesis describes studies on the use of the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS) developed and tested against field data by Bomblies et al. (2008) in simulating and predicting the potential for malaria transmission in rural Africa. The first study examined the temporal resolution of rainfall input required by HYDREMATS. Simulations conducted over Banizoumbou village in Niger showed that for reasonably accurate simulation of mosquito populations, the model requires rainfall data with at least 1 hour resolution. The second study investigated whether HYDREMATS could be effectively forced by satellite based estimates of rainfall instead of ground based observations. The CPC Morphing technique (CMORPH) (Joyce et al., 2004) precipitation estimates distributed by NOAA are available at a 30-minute temporal resolution and 8 km spatial resolution. We compared mosquito populations simulated by HYDREMATS when the model is forced by adjusted CMORPH estimates and by ground observations. The results indicate that adjusted CMORPH rainfall estimates can be used with HYDREMATS to simulate the dynamics of mosquito populations and malaria transmission with accuracy similar to that obtained when using ground observations of rainfall. The third study tested the ability of HYDREMATS to make short term predictions about mosquito populations. A method was developed by which the rainfall forcing for HYDREMATS is constructed to suit a prediction mode. Observed rainfall is used up until the date of the prediction. The rainfall for the following two weeks (or four weeks) is assumed to be the seasonal mean for that period. HYDREMATS predictions using this method were not significantly different from simulations using observed data.by Teresa K. Yamana.S.M

The desertification context

Desertification is a critical issue for Mediterranean drylands. Climate change is expected to aggravate its extension and severity by reinforcing the biophysical driving forces behind desertification processes: hydrology, vegetation cover and soil erosion. The main objective of this thesis is to assess the vulnerability of Mediterranean watersheds to climate change, by estimating impacts on desertification drivers and the watersheds’ resilience to them. To achieve this objective, a modeling framework capable of analyzing the processes linking climate and the main drivers is developed. The framework couples different models adapted to different spatial and temporal scales. A new model for the event scale is developed, the MEFIDIS model, with a focus on the particular processes governing Mediterranean watersheds. Model results are compared with desertification thresholds to estimate resilience. This methodology is applied to two contrasting study areas: the Guadiana and the Tejo, which currently present a semi-arid and humid climate. The main conclusions taken from this work can be summarized as follows: • hydrological processes show a high sensitivity to climate change, leading to a significant decrease in runoff and an increase in temporal variability; • vegetation processes appear to be less sensitive, with negative impacts for agricultural species and forests, and positive impacts for Mediterranean species; • changes to soil erosion processes appear to depend on the balance between changes to surface runoff and vegetation cover, itself governed by relationship between changes to temperature and rainfall; • as the magnitude of changes to climate increases, desertification thresholds are surpassed in a sequential way, starting with the watersheds’ ability to sustain current water demands and followed by the vegetation support capacity; • the most important thresholds appear to be a temperature increase of +3.5 to +4.5 ºC and a rainfall decrease of -10 to -20 %; • rainfall changes beyond this threshold could lead to severe water stress occurring even if current water uses are moderated, with droughts occurring in 1 out of 4 years; • temperature changes beyond this threshold could lead to a decrease in agricultural yield accompanied by an increase in soil erosion for croplands; • combined changes of temperature and rainfall beyond the thresholds could shift both systems towards a more arid state, leading to severe water stresses and significant changes to the support capacity for current agriculture and natural vegetation in both study areas.Supported by the Portuguese Foundation for Science and Technology and the European Union under Operational Program “Science and Innovation” (POCI 2010), Ph.D. grant ref. SFRH/BD/5059/200