Drought is the most costly hazard among all natural disasters. Despite the significant improvements in drought modeling over the last decade, accurate provisions of drought conditions in a timely manner is still one of the major research challenges. In order to improve the current drought monitoring and forecasting skills, this study presents a hybrid system with a combination of remotely sensed data assimilation based on particle filtering and a probabilistic drought forecasting model. Besides the proposed drought monitoring system through land data assimilation, another novel aspect of this dissertation is to seek the use of data assimilation to quantify land initial condition uncertainty rather than relying entirely on the hydrologic model or the land surface model to generate a single deterministic initial condition. Monthly to seasonal drought forecasting products are generated using the updated initial conditions. The computational complexity of the distributed data assimilation system required a modular parallel particle filtering framework which was developed and allowed for a large ensemble size in particle filtering implementation. The application of the proposed system is demonstrated with two case studies at the regional (Columbia River Basin) and the Conterminous United States. Results from both synthetic and real case studies suggest that the land data assimilation system significantly improves drought monitoring and forecasting skills. These results also show how sensitive the seasonal drought forecasting skill is to the initial conditions, which can lead to better facilitation of the state/federal drought preparation and response actions.

Drought is perceived differently by different people, but in general, it is conceived as a period of below normal precipitation or moisture deficiency that would affect the social and economic activities of a region. Many numerical indices are used to quantify the effect of drought. The Pahner Drought Severity Index (PDSI) is the most and widely used drought indicator parameter in most recent applications. The PDSI takes into account precipitation, temperature, and soil moisture and depicts prolonged abnormal dryness or wetness. A Markov chain model was developed to analyze the likelihood of occurrences of the seven types of weather spells, defined by the National Oceanic and Atmospheric Administration (NOAA). The spells are classified, using the PDSI computed monthly by the NOAA. The model predicts both short and long term drought status over an entire climatic division. Twelve monthly transition matrices and one annual transition matrix were computed. The matrices show the transition patterns between months and between drought states. The model was applied to the Tidewater area (climatic division l) and the Southwest mountains (climatic division 6) of Virginia. The model predictions reflect the reality and compare very well with the observed data for these two climatic divisions. This model can potentially be used as a tool for water resource planning and design of drought assistance plans by water resource managers. / Master of Science

Drought is one of the most severe natural disasters and detrimentally impacts water resources, agricultural production, the environment, and the economy. Climate change is expected to influence the frequency and severity of extreme droughts. This dissertation evaluates drought conditions using a variety of hydrologic modeling approaches include short-term drought forecasting, long-term drought projection, and a coupled surface-groundwater dynamic drought assessment. The economic impacts of drought are also explored through a linked economic impact model. Study results highlight the need for various drought assessment approaches and provide insights into the array of tools and techniques that can be employed to generate decision-support tools for drought mitigation plans and water resource allocation. For short-term drought forecasting, the Soil and Water Assessment Tool (SWAT) and Variable Infiltration Capacity (VIC) models are used with a meteorological forecasting dataset. Results indicate that eight weeks of lead-time drought forecasting show good drought predictability for the Contiguous United States (CONUS). For the drought projection at a finer scale, both SWAT and VIC models are applied with Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model outputs to derive multiple drought indices for the Chesapeake Bay watershed and five river basins in Virginia. The results indicate that current climate change projections will lead to increased drought in the entire Chesapeake Bay watershed and Virginia river basins because of increases in the sum of evapotranspiration, and surface and groundwater discharge. The impacts of climate change on future agricultural droughts and associated economy-wide implications are then evaluated using the VIC and IMPLAN (IMpact analysis for PLANning) model for the several congressional districts in Virginia. The result indicated that increases in agricultural drought in the future would lead to decreases in agricultural productions and job losses. Finally, a coupled framework using the VIC and MODFLOW models is implemented for the Chesapeake Bay and the Northern Atlantic Coastal Plain aquifer system, and the results of a drought index that incorporates groundwater conditions performs better for some drought periods. Hydrologic modeling framework with multiple hydrologic models and various scales can provide a better understanding of drought assessments because the comparisons and contrasts of diverse methods are available. / PHD / Drought is one of the most severe natural hazards and negatively impacts the water resources, agricultural production, the environment, and the economy. Climate change influences the frequency and severity of extreme droughts. This dissertation assesses drought conditions using various hydrologic-modeling methods, which are drought forecasting, climate change impacts on drought, economic influences of droughts, and a coupled model approach. Study results highlight the need for various drought evaluation techniques that can generate decision-support tools for drought mitigation plans and water resource management. For short-term drought forecasting, two hydrologic models are used with a meteorological forecasting dataset. Results indicate that eight weeks of lead-time drought forecasting show good drought predictability for the Contiguous United States (CONUS). For the drought projection at a finer scale, two models are also used with multiple climate models for the Chesapeake Bay (CB) watershed and five river basins in Virginia. The results indicate that current climate change projections will lead to increased drought in the entire CB watershed and Virginia river basins. The impacts of climate change on future agricultural droughts and associated economy-wide implications are then evaluated using the hydrologic and economic models for the several congressional districts in Virginia. The results indicate that increases in agricultural drought in the future would lead to decreases in agricultural productions and job losses. Finally, a coupled model is implemented for the CB and the Northern Atlantic Coastal Plain (NACP) aquifer system, and the results of a drought index that incorporate groundwater conditions performs better for some drought periods. Hydrologic modeling framework with multiple hydrologic models and various scales can provide a better understanding of drought assessments because the comparisons and contrasts of diverse methods are available.

Drought is a recurrent climatic process that occurs with uneven temporal and spatial characteristics over broad areas and over an extended period of time. Therefore, detecting drought onsets and ends as well as assessing drought severity using satellite-derived information is essential. This should be especially the case in an arid country like Namibia where drought is part of Namibia’s climatology. It is believed that proper planning and research using near real-time data can curb the devastating environmental and socio-economic impacts of drought. Weather data used currently are often from a very sparse meteorological network, incomplete and/or not always available in good time to enable delineating accurately and timely, regional- and local-scale droughts. Consequently, the detection and monitoring efforts are hampered to provide timely and unbiased information to decision makers for accurate drought relief allocation and for land reform purposes. Furthermore, even though, data obtained from satellite-based sensors such as the Advanced Very High Resolution Radiometer (AVHRR) have been studied as a tool for drought monitoring for many years and provides an extensive temporal record for comparison, its coarse spatial resolution limits its effectiveness at detecting local scale variability where severe droughts might go undetected due to these data constraints. The objective of this study was to evaluate satellite-based and meteorological drought indices for the spatial and temporal detection, assessment and monitoring of drought condition to accurately delineate drought characteristics of drought prone areas. The study computed the Vegetation Condition Index (VCI) and Normalized Difference Vegetation Index (NDVI) from the 250m resolution NDVI data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and one- and three-months Standardized Precipitation Index (SPI) data from rainfall stations in the study area. Detailed analyses of spatial and temporal drought dynamics during three seasons (2005/6 - wet, 2006/7 - normal and 2007/8 - dry) have been carried out through index maps generated in a Geographic Information Systems (GIS) environment from the mentioned data. Analysis and interpretation of these maps, which give different drought scenarios, reveal that remotely-sensed drought indices can accurately detect and map the local and regional drought spatial occurrence. Moreover, statistical analysis found strong correlations between the regional crop production data and the remotely-sensed data. However, the results showed that the local and regional drought occurrences detected were not reflected in national crop production data, confirming the suspicion that important local spatial variations are only detected if higher spatial resolution data are used. The study concluded that fine spatial resolution satellite data should be used to aid decision makers in monitoring and detecting drought which will also aid the allocation of millions of dollars in drought relief funds. / http://hdl.handle.net/10413/534 / Thesis (M.Env.Dev.) - University of KwaZulu-Natal, Pietermaritzburg, 2009.

Agricultural drought is a creeping disaster that overshadows the vegetative cover in general and cropland specifically in Iraq, a country that was well known for its agricultural production and fertile soil. In the recent years, the arable lands in Iraq experienced increasing land degradation that led to desertification, economic losses, food insecurity, and deteriorating environment. Remote sensing is employed in this study and four different indices are utilized, each of which is derived from MODIS satellite mission products. Agricultural drought maps are produced from 2003 to 2015 after masking the vegetation cover. Year 2008 was found the most severe drought year during the study period, where drought covered 37% of the vegetated land. This part of the study demonstrated the capability of remote sensing in fulfilling the need of an early warning system for agricultural drought over such a data-scarce region. This study also aims to monitor hydrological drought. The Gravity Recovery and Climate Experiment (GRACE) satellite-derived monthly Terrestrial Water Storage (TWS) is the hydrological drought indicator, that is used to calculate the deficit. Severity of drought events are calculated by integrating monthly water deficit over the drought period. In addition, drought recovery time is assessed depending on the estimated deficit. Major drought events are classified into several levels of severity by applying a drought monograph approach. The results demonstrated that GRACE TWS is a reliable indicator for drought assessment over Iraq, and provides useful information for decision makers which can be utilized in developing drought adaptation and mitigation strategies.

The sertao of northeast Brazil is a semiarid region characterized by recurring droughts. The vastness of the area (650,000 km$ sp2)$ poses a challenge to the effective monitoring of the impacts of drought at a scale that would be useful to the inhabitants of the sertao. Remote sensing data provide a viable way of assessing the extent and nature of drought across the landscape. / The work present a more effective algorithm to estimate rainfall from both the cold and warm cloud types present. Using a decision-tree methodology, the analysis yields rainfall estimates over the 0-21 mm range. Because seasonal variations in rainfall produce differences in vegetation, soils and hydrologic responses, Principal Components Analysis was used to examine these land surface responses. Individual components and component pairings were useful in identifying variations in vegetation density, geobotanical differences and drainage characteristics. The presence of cloud cover was found to dampen the land surface information that could be extracted. Landsat Thematic Mapper (TM) imagery was then used to produce a moisture index which characterizes surface wetness in relation to other features present in a scene. The multispectral combination of TM bands 1, 4 and 6 allowed for the separation of the surface types present, in locational space. This space was defined by an open-ended triange made up of a vertical "water line", a horizontal line of equal vegetation density; and a negatively-slopping iso-moisture line. The stability of the moisture index was influenced by varying scale and seasonal conditions. / In the drought conditions that prevailed in 1991-1992, these methods provide important additions to existing drought monitoring approaches in the Brazilian northeast. Further calibration is required in order to extend their applicability to other geographical regions and time frames.

No description available.

Forecasts of hydrological information are vital for many of society's functions. Availability of water is a requirement for any civilization, and this necessitates quantitative estimates of water for effective resource management. The research in this dissertation will focus on the forecasting of hydrological quantities, with emphasis on times of anomalously low water availability, commonly referred to as droughts. Of particular focus is the quantification of uncertainty in hydrological forecasts, and the factors that affect that uncertainty. With this focus, Bayesian methods, including ensemble data assimilation and multi-model combinations, are utilized to develop a probabilistic forecasting system. This system is applied to the upper Colorado River Basin for water supply and drought forecast analysis. This dissertation examines further advancements related to the identification of drought intensity. Due to the reliance of drought forecasting on measures of the magnitude of a drought event, it is imperative that these measures be highly accurate. In order to quantify drought intensity, hydrologists typically use statistical indices, which place observed hydrological deficiencies within the context of historical climate. Although such indices are a convenient framework for understanding the intensity of a drought event, they have obstacles related to non-stationary climate, and non-uniformly distributed input variables. This dissertation discusses these shortcomings, demonstrates some errors that conventional indices may lead to, and then proposes a movement towards physically-based indices to overcome these issues. A final advancement in this dissertation is an examination of the sensitivity of hydrological forecasts to initial conditions. Although this has been performed in many recent studies, the experiment here takes a more detailed approach. Rather than determining the lead time at which meteorological forcing becomes dominant with respect to initial conditions, this study quantifies the lead time at which the forecast becomes entirely insensitive to initial conditions, and estimating the rate at which the forecast loses sensitivity to initial conditions. A primary goal with this study is to examine the recovery of drought, which is related to the loss of sensitivity to below average initial moisture conditions over time. Through this analysis, it is found that forecasts are sensitive to initial conditions at greater lead times than previously thought, which has repercussions for development of forecast systems.

Recent water scarcities across the southwestern U.S. with severe effects on the living environment inspire the development of new methodologies to achieve reliable drought forecasting in seasonal scale. Reliable forecast of hydrologic variables, in general, is a preliminary requirement for appropriate planning of water resources and developing effective allocation policies. This study aims at developing new techniques with specific probabilistic features to improve the reliability of hydrologic forecasts, particularly the drought forecasts. The drought status in the future is determined by certain hydrologic variables that are basically estimated by the hydrologic models with rather simple to complex structures. Since the predictions of hydrologic models are prone to different sources of uncertainties, there have been several techniques examined during past several years which generally attempt to combine the predictions of single (multiple) hydrologic models to generate an ensemble of hydrologic forecasts addressing the inherent uncertainties. However, the imperfect structure of hydrologic models usually lead to systematic bias of hydrologic predictions that further appears in the forecast ensembles. This study proposes a post-processing method that is applied to the raw forecast of hydrologic variables and can develop the entire distribution of forecast around the initial single-value prediction. To establish the probability density function (PDF) of the forecast, a group of multivariate distribution functions, the so-called copula functions, are incorporated in the post-processing procedure. The performance of the new post-processing technique is tested on 2500 hypothetical case studies and the streamflow forecast of Sprague River Basin in southern Oregon. Verified by some deterministic and probabilistic verification measures, the method of Quantile Mapping as a traditional post-processing technique cannot generate the qualified forecasts as comparing with the copula-based method. The post-processing technique is then expanded to exclusively study the drought forecasts across the different spatial and temporal scales. In the proposed drought forecasting model, the drought status in the future is evaluated based on the drought status of the past seasons while the correlations between the drought variables of consecutive seasons are preserved by copula functions. The main benefit of the new forecast model is its probabilistic features in analyzing future droughts. It develops conditional probability of drought status in the forecast season and generates the PDF and cumulative distribution function (CDF) of future droughts given the past status. The conditional PDF can return the highest probable drought in the future along with an assessment of the uncertainty around that value. Using the conditional CDF for forecast season, the model can generate the maps of drought status across the basin with particular chance of occurrence in the future. In a different analysis of the conditional CDF developed for the forecast season, the chance of a particular drought in the forecast period can be approximated given the drought status of earlier seasons. The forecast methodology developed in this study shows promising results in hydrologic forecasts and its particular probabilistic features are inspiring for future studies.

The world’s freshwater resources are being placed under increasing pressure owing to growth in population, economic development, improved standards of living, agricultural intensification (linked mainly to irrigation), pollution and mismanagement of available freshwater resources. Already, in many parts of the Orange River Catchment, water availability has reached a critical stage. It has become increasingly evident that water related problems can no longer be resolved by water managers alone, owing to the problems becoming more interconnected with other development related issues, as well as with social, economic, environmental, legal and political factors. With the advent of climate change and the likelihood of increases in extreme events, water managers’ awareness of uncertainties and critical reflections on the adequacy of current management approaches is increasing. In order to manage water resources effectively a more holistic approach is required than has hitherto been the case, in which technological, social and economic development are linked with the protection of natural ecosystems and with dependable projections of future climatic conditions. To assess the climate risk connected with rural and urban water management, and to develop adaptive strategies that can respond to an increasingly variable climate that is projected into the future and help to reduce adverse impacts, it is necessary to make connections between climate related hazards, climate forecasts as well as climate change, and the planning, design, operation, maintenance, and rehabilitation of water related infrastructure. Therefore, adaptive water resources management (AWRM), which in essence is “learning by doing”, is believed to be a timely extension of the integrated water resources management (IWRM) approach as it acknowledges uncertainty and is flexible in that it allows for the adjustment of actions based on information learned about the system. Furthermore, it is suggested that climate risk management be imbedded within the AWRM framework. The objective of the research presented in this thesis is to develop techniques to integrate state-of-the-art climate projection scenarios – which forms part of the first step of the adaptive management cycle – downscaled to the regional/local scale, with hydro-climatic hazard determination – which forms part of the first step in the risk management process – in order to simulate projected impacts of climate change on hydro-climatic hazards in the Orange River Catchment (defined in this study as those areas of the catchment that exist within South Africa and Lesotho). The techniques developed and the results presented in this study can be used by decision-makers in the water sector in order to make informed proactive decisions as a response to projected future impacts of hydro-climatic hazards – all within a framework of AWRM. Steps towards fulfilling the above-mentioned objective begins by way of a comprehensive literature review; firstly of the study area, where it is identified that the Orange River Catchment is, in hydro-climatic terms, already a high risk environment; and secondly, of the relevant concepts involved which are, for this specific study, those pertaining to climate change, and the associated potential hydro-climatic impacts. These include risk management and its components, in order identify how hazard identification fits into the broader concept of risk management; and water resources management practices, in order to place the issues identified above within the context of AWRM. This study uses future projections of climate from five General Circulation Models, all using the SRES A2 emission scenario. By and large, however, where techniques developed in this study are demonstrated, this is done using the projections from the ECHAM5/MPI-OM GCM which, relative to the other four available GCMs, is considered to provide “middle of the road” projections of future climates over southern Africa. These climate projections are used in conjunction with the locally developed and widely verified ACRU hydrological model, as well as a newly developed hydro-climatic database at a finer spatial resolution than was available before, to make projections regarding the likelihood and severity of hydro-climatic hazards that may occur in the Orange River Catchment. The impacts of climate change on hydro-climatic hazards, viz. design rainfalls, design floods, droughts and sediment yields are investigated, with the results including a quantitative uncertainty analysis, by way of an index of concurrence from multiple GCM projections, for each of the respective analyses. A new methodology for the calculation of short duration (< 24 hour) design rainfalls from daily GCM rainfall projections is developed in this study. The methodology utilises an index storm approach and is based on L-moments, allowing for short duration design rainfalls to be estimated at any location in South Africa for which daily GCM rainfall projections exist. The results from the five GCMs used in this study indicate the following possible impacts of climate change on hydro-climatic hazards in the Orange River Catchment: · Design rainfalls of both short and long duration are, by and large, projected to increase by the intermediate future period represented by 2046 - 2065, and even more so by the more distant future period 2081 - 2100. · Design floods are, by and large, projected to increase into the intermediate future, and even more into the more distant future; with these increases being larger than those projected for design rainfalls. · Both meteorological and hydrological droughts are projected to decrease, both in terms of magnitude and frequency, by the period 2046 - 2065, with further decreases projected for the period 2081 - 2100. Where increases in meteorological and hydrological droughts are projected to occur, these are most likely to be in the western, drier regions of the catchment. · Annual sediment yields, as well as their year-to-year variability, are projected to increase by the period 2046 - 2065, and even more so by the period 2081 - 2100. These increases are most likely to occur in the higher rainfall, and especially in the steeper, regions in the east of the catchment. Additionally, with respect to the above-mentioned hydro-climatic hazards, it was found that: · The statistic chosen to describe inter-annual variability of hydro-climatic variables may create different perceptions of the projected future hydroclimatic environment and, hence, whether or not the water manager would decide whether adaptive action is necessary to manage future variability. · There is greater uncertainty amongst the GCMs used in this study when estimating design events (rainfall and streamflow) for shorter durations and longer return periods, indicating that GCMs may still be failing to simulate individual extreme events. · The spatial distribution of projected changes in meteorological and hydrological droughts are different, owing to the complexities introduced by the hydrological system · Many areas may be exposed to increases in hydrological hazards (i.e. hydrological drought, floods and/or sediment yields) because, where one extreme is projected to decrease, one of the others is often projected to increase. The thesis is concluded with recommendations for future research in the climate change and hydrological fields, based on the experiences gained in undertaking this study. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2012.