Generation of Monthly Precipitation Under Climate Change for the Upper Blue Nile River Basin,Ethiopia1

Abstract: This work develops a methodology to project the future precipitation in large river basins under limited data and climate change while preserving the historical temporal and spatial characteristics. The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain‐fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long‐term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100‐year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be ?7 to 28% with a mean increase of about 11%. The seasonal results showed increasing wet conditions in all seasons with changes of mean precipitation of 5, 47, and 6% for wet, dry, and mild seasons, respectively.     

Robustness of Meteorological Droughts in Dynamically Downscaled Climate Simulations

We examine the robustness of a suite of regional climate models (RCMs) in simulating meteorological droughts and associated metrics in present‐day climate (1971‐2003) over the conterminous United States (U.S.). The RCMs that are part of North American Regional Climate Change Assessment Program (NARCCAP) simulations are compared with multiple observations over the climatologically homogeneous regions of the U.S. The seasonal precipitation, climatology, drought attributes, and trends have been assessed. The reanalysis‐based multi‐model median RCM reasonably simulates observed statistical attributes of drought and the regional detail due to topographic forcing. However, models fail to simulate significant drying trend over the Southwest and West. Further, reanalysis‐based NARCCAP runs underestimate the observed drought frequency overall, with the exception of the Southwest; whereas they underestimate persistence in the drought‐affected areas over the Southwest and West‐North Central regions. However, global climate model‐driven NARCCAP ensembles tend to overestimate regional drought frequencies. Models exhibit considerable uncertainties while reproducing meteorological drought statistics, as evidenced by a general lack of agreement in the Hurst exponent, which in turn controls drought persistence. Water resources managers need to be aware of the limitations of current climate models, while regional climate modelers may want to fine‐tune their parameters to address impact‐relevant metrics.     

Irrigation Water Allocation Using an Inexact Two‐Stage Quadratic Programming with Fuzzy Input under Climate Change

Agricultural irrigation accounts for nearly 70% of the total water use around the world. Uncertainties and climate change together exacerbate the complexity of optimal allocation of water resources for irrigation. An interval‐fuzzy two‐stage stochastic quadratic programming model is developed for determining the plans for water allocation for irrigation with maximum benefits. The model is shown to be applicable when inputs are expressed as discrete, fuzzy or random. In order to reflect the effect of marginal utility on benefit and cost, the model can also deal with nonlinearities in the objective function. Results from applying the model to a case study in the middle reaches of the Heihe River basin, China, show schemes for water allocation for irrigation of different crops in every month of the crop growth period under various flow levels are effective for achieving high economic benefits. Different climate change scenarios are used to analyze the impact of changing water requirement and water availability on irrigation water allocation. The proposed model can aid the decision maker in formulating desired irrigation water management policies in the wake of uncertainties and changing environment.     

Characterizing Drought in Irrigated Agricultural Systems: The Surface Water Delivery Index (SWDI)

Quantifying surface water shortages in arid and semiarid agricultural regions is challenging because limited water supplies are distributed over long distances based on complex water management systems constrained by legal, economic, and social frameworks that evolve with time. In such regions, the water supply is often derived in a climate dramatically different from where the water is diverted to meet agricultural demand. The existing drought indices which rely on local climate do not portray the complexities of the economic and legal constraints on water delivery. Nor do these indices quantify the shortages that occur in drought. Therefore, this research proposes a methodological approach to define surface water shortages in irrigated agricultural systems using a newly developed index termed the Surface Water Delivery Index (SWDI). The SWDI can be used to uniformly quantify surface water deficits/shortages at the end of the irrigation season. Results from the SWDI clearly illustrate how water shortages in droughts identified by the existing indices (e.g., SPI and PDSI) vary strongly both within and between basins. Some surface water entities are much more prone to water shortages than other entities based both on their source of water supply and water right portfolios.     

Variability and Trends in Runoff Efficiency in the Conterminous United States

Variability and trends in water‐year runoff efficiency (RE) — computed as the ratio of water‐year runoff (streamflow per unit area) to water‐year precipitation — in the conterminous United States (CONUS) are examined for the 1951 through 2012 period. Changes in RE are analyzed using runoff and precipitation data aggregated to United States Geological Survey 8‐digit hydrologic cataloging units (HUs). Results indicate increases in RE for some regions in the north‐central CONUS and large decreases in RE for the south‐central CONUS. The increases in RE in the north‐central CONUS are explained by trends in climate, whereas the large decreases in RE in the south‐central CONUS likely are related to groundwater withdrawals from the Ogallala aquifer to support irrigated agriculture.     

Evaluating Infiltration Requirements for New Development Using Extreme Storm Transposition: A Case Study from Dane County,WI

Changes in land use and extreme rainfall trends can lead to increased flood vulnerability in many parts of the world, especially for urbanized watersheds. This study investigates the performance of existing stormwater management strategies for the Upper Yahara watershed in Dane County, WI to determine whether they are adequate to protect urban and suburban development from an extreme rainfall. Using extreme storm transposition, we model the performance of the stormwater infiltration practices required for new development under current county ordinances. We find during extreme rainfall the volume of post‐development runoff from impervious surfaces from a typical site would increase by over 55% over pre‐development conditions. We recommend the ordinance be strengthened to reduce vulnerability to flooding from future urban expansion and the likely increase in the magnitude and frequency of extreme storms.     

Water Management Decision Making in the Face of Multiple Forms of Uncertainty and Risk

In the Wasatch Range Metropolitan Area of Northern Utah, water management decision makers confront multiple forms of uncertainty and risk. Adapting to these uncertainties and risks is critical for maintaining the long‐term sustainability of the region's water supply. This study draws on interview data to assess the major challenges climatic and social changes pose to Utah's water future, as well as potential solutions. The study identifies the water management adaptation decision‐making space shaped by the interacting institutional, social, economic, political, and biophysical processes that enable and constrain sustainable water management. The study finds water managers and other water actors see challenges related to reallocating water, including equitable water transfers and stakeholder cooperation, addressing population growth, and locating additional water supplies, as more problematic than the challenges posed by climate change. Furthermore, there is significant disagreement between water actors over how to best adapt to both climatic and social changes. This study concludes with a discussion of the path dependencies that present challenges to adaptive water management decision making, as well as opportunities for the pursuit of a new water management paradigm based on soft‐path solutions. Such knowledge is useful for understanding the institutional and social adaptations needed for water management to successfully address future uncertainties and risks.     

Impacts of Changes in Precipitation Amount and Distribution on Water Resources Studied Using a Model Rainwater Harvesting System

Water supply reliability is expected to be affected by both precipitation amount and distribution changes under recent and future climate change. We compare historical (1951‐2010) changes in annual‐mean and annual‐maximum daily precipitation in the global set of station observations from Global Historical Climatology Network and climate models from the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP), and develop the study to 2011‐2099 for model projections under high radiative forcing scenario (RCP8.5). We develop a simple rainwater harvesting system (RWHS) model and drive it with observational and modeled precipitation. We study the changes in mean and maximum precipitation along with changes in the reliability of the model RWHS as tools to assess the impact of changes in precipitation amount and distribution on reliability of precipitation‐fed water supplies. Results show faster increase in observed maximum precipitation (10.14% per K global warming) than mean precipitation (7.64% per K), and increased reliability of the model RWHS driven by observed precipitation by an average of 0.2% per decade. The ISI‐MIP models show even faster increase in maximum precipitation compared to mean precipitation. However, they imply decreases in mean reliability, for an average 0.15% per decade. Compared to observations, climate models underestimate the increasing trends in mean and maximum precipitation and show the opposite direction of change in reliability of a model water supply system.     

Perceived mastery climate,felt trust,and knowledge sharing

Interpersonal trust is associated with a range of adaptive outcomes, including knowledge sharing. However, to date, our knowledge of antecedents and consequences of employees feeling trusted by supervisors in organizations remains limited. On the basis of a multisource, multiwave field study among 956 employees from 5 Norwegian organizations, we examined the predictive roles of perceived mastery climate and employee felt trust for employees' knowledge sharing. Drawing on the achievement goal theory, we develop and test a model to demonstrate that when employees perceive a mastery climate, they are more likely to feel trusted by their supervisors at both the individual and group levels. Moreover, the relationship between employees' perceptions of a mastery climate and supervisor‐rated knowledge sharing is mediated by perceptions of being trusted by the supervisor. Theoretical contributions and practical implications of our findings are discussed.     

An assessment of safety climate,job satisfaction and turnover intention relationships using a national sample of workers from the USA

The association between safety climate, job satisfaction and turnover intention has not been thoroughly researched. This research is needed so that safety researchers and practitioners can begin to delineate the impact of safety on organizational and business outcomes. A path analysis was completed using data from a national sample of workers from the USA (n?=?1525). The overall fit of the model was excellent and analyses determined that both training and resource adequacy positively affected safety climate and job satisfaction. Safety climate also positively influenced job satisfaction. Both safety climate and job satisfaction were negatively associated with respondents’ turnover intention. In the study, the relationship between job satisfaction and turnover intention is reiterated in a sample of workers across many industries. This study is novel because it is one of the first studies to confirm that turnover intention is reduced with increased safety climate in a diverse sample of workers.