There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The ERA5 global reanalysis

Soil Chemical Analysis

Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Climate change 2014 - Synthesis Report

Almost all observational studies report that extreme precipitation in the U.S. has increased in magnitude over the last several decades (Groisman et al. 1999, 2013; Kunkel et al. 2013). Although such studies differ regarding the geographic influence of trends, the consensus on extreme precipitation (i.e., short-term events with a 5% exceedance probability or less) is surprisingly unequivocal. The American Meteorological Society (AMS) 2013 State of Knowledge report on extreme precipitation (Kunkel et al. 2013) states that “There is strong evidence for a nationally averaged upward trend in the frequency and intensity of extreme precipitation events : : : ” and that the in situ measurement network is considered “adequate to detect such trends.” In a slightly more recent study, Groisman et al. (2013) reported that the very heavy precipitation rates (in the upper 5% of all records) have increased over approximately two-thirds of the eastern U.S. during the last 30 years, whereas the number of days with maximum daily convective available potential energy (CAPE) values exceeding 1,500 J=kg has increased ∼30% in the same period during the spring season. Although the potential causes of this increasing trend may be multifactorial, the most recent AMS report also indicates increasing atmospheric water vapor as a leading causative factor (Kunkel et al. 2013). Despite the overwhelming consensus on the rising trend of extreme precipitation rates, the response of peak stream flow is not as unequivocal. Although regulation of surface flow, increasing imperviousness, and altering infiltration rates through land cover change are some of the many ways peak flow distribution can be impacted during a stationary precipitation regime, a clear signal of the rising trend of extreme precipitation may be expected in peak flow records. However, the paradox of peak flow is that any rising trend in peak flows is much more elusive to observe over statistically significant locations. Vogel et al. (2011) analyzed as many as 14,000 U.S. streamflow records and found statistically significant increases in flood risk at only approximately 10% of the stations. They also attributed most of this increase to hydrologic changes in land cover (increasing imperviousness, and hence, increased surface runoff generation) rather than global warming. Villarini et al. (2009) analyzed 50 stream flow stations with more than a centurylong record of flow observations using sophisticated methods for change point detection, trend analysis, and nonstationarity. However, they concluded that “it is easier to proclaim the demise of stationarity of flood peaks than to prove it through analyses of annual flood peak data.” Hydrologic extremes are the foundation of most design, operation, and risk management of water management systems that currently serve society. Yet, the current hydrology that traditionally models only the natural laws of physics of a watershed has become increasingly limited in its ability to provide relevant answers for emerging changes that are observed (Vogel 2011). This is because traditional hydrology continues to assume that the extensive replumbing of the natural water network, along with changes to land cover and hemispheric forcing of climate change attributable to extensive human activity, are only an external forcing rather than an integral part of the coupled human–natural system. The massive but gradual redistribution of water through artificial reservoirs, numerous irrigation schemes, land cover change, and urbanization since the early 1900s has resulted in a nonnegligible contribution to increased moisture availability and altered atmospheric convergence patterns overland in the U.S. (Puma and Cook 2010; DeAngelis et al. 2010). For example, USGS records (Kenny et al. 2009) indicate an increase in irrigation acreage from 141; 000 km (in 1950) to 263; 000 km (in 2005). The latter is equivalent to a withdrawal of 144 million acre-ft (or 177 km) of surface and ground water per year that evaporates directly to the atmosphere [and may be recycled as precipitation (Eltahir and Bras 1996)], and likely balances any increases in peak flow attributable to rising precipitation rates. Similarly, approximately 75,000 artificial reservoirs were built in the U.S. during the last century, with a total capacity almost equaling one year of mean runoff (Graf 1999, 2006; Global Water Systems Project 2008). The cumulative effect of these extensive impoundments has been to triple the average residence time of surface water from 0.1 years (in 1900) to 0.3 years in 2000 (Vorosmarty and Sahagian 2000), an aspect that clearly has not received attention during the assessment of peak flow trends. Similar large-scale alterations have happened to the natural land cover in the U.S., which have modified both hydrologic behavior (water partitioning) and radiative behavior (energy partitioning) in nonnegligible amounts (Pielke et al. 2011). The explicit consideration of the human replumbing of natural water systems may only explain part of the peak flow paradox. For example, global warming may intensify evaporative fluxes and initiate regional drying of soils. This would compensate for increasing precipitation rates through increased abstraction of precipitation and potentially lowering of peak flows. Thus, the hydrologic engineering community may need to employ a multifactorial approach involving, as a fundamental premise, the human impact (feedback) on local-to-regional weather and climate that have been researched for over many decades (Pielke 2009; Mahmood et al. 2010), but overlooked in most studies of hydrologic extremes (Hossain et al. 2012). Understanding the causative factors behind the historical evolution of extreme precipitation and peak flow is probably the most urgent priority for current hydrologists to adapt the design and operation of infrastructure. Through an investigation of the combined role of this land–atmosphere feedback owing to artificial redistribution of water, land cover change and climate change, one may postulate that the paradox of peak flows not having responded in sync with rising extreme precipitation may be better understood. Consequently, this approach may allow the hydrologic engineering community to understand how peak flow patterns, which are used in the frequency analysis and design of

Paradox of Peak Flows in a Changing Climate

1.0 INTRODUCTION Floods account for about 15 % of the total death toll related to natural disasters, wherein typically more than 10 million lives are either displaced or lost each year internationally (Hossain, 2006). Rainfall is the primary determinant of floods and its intimate interaction with the landform (i.e., topography, vegetation and channel network) magnified by highly wet antecedent conditions leads to catastrophic flooding in medium (i.e., 1000 ~ 5000 km) and large (i.e., > 5000 km) river basins. Furthermore, floods are more destructive over tropical river basins that lack adequate surface stations necessary for real-time rainfall monitoring – i.e., the ungauged river basins (Hossain and Katiyar, 2006a) (see Figure 1, left panel). However, flood prediction is becoming ever more challenging in these mediumto-large river basins due to the systematic decline of in situ rainfall networks world-wide. The gradual erosion of these conventional rainfall data sources has lately been recognized as a major concern for advancing hydrologic monitoring, especially in basins that are ungauged or already sparsely instrumented (Stokstad, 1999; Shikhlomanov et al., 2002). As a collective response, the hydrologic community have recently established partnerships for the development of space-borne missions for cost-effective, yet global,

Advancing the Use of Satellite Rainfall Datasets for Flood Prediction in Ungauged Basins: The Role of Scale, Hydrologic Process Controls and the Global Precipitation Measurement Mission

Study of Antioxidative Properties of Some Mono Amino-Acid-Type and Dipeptide-Type Surfactants

Understanding Volumetric Water Storage in Monsoonal Wetlands of Northeastern Bangladesh

Providing error information associated with existing satellite precipitation estimates is crucial to advancing applications in hydrologic modeling. In this study, we present a method of estimating the square difference prediction of satellite precipitation (hereafter used synonymously with “error variance”) using regression model for three satellite precipitation products (3B42RT, CMORPH, and PERSIANN-CCS) using easily available geophysical features and satellite precipitation rate. Building on a suite of recent studies that have developed the error variance models, the goal of this work is to explore how well the method works around the world in diverse geophysical settings. Topography, climate, and seasons are considered as the governing factors to segregate the satellite precipitation uncertainty and fit a nonlinear regression equation as a function of satellite precipitation rate. The error variance models were tested on USA, Asia, Middle East, and Mediterranean region. Rain-gauge based precipitation product was used to validate the error variance of satellite precipitation products. The regression approach yielded good performance skill with high correlation between simulated and observed error variances. The correlation ranged from 0.46 to 0.98 during the independent validation period. In most cases (~ 85% of the scenarios), the correlation was higher than 0.72. The error variance models also captured the spatial distribution of observed error variance adequately for all study regions while producing unbiased residual error. The approach is promising for regions where missed precipitation is not a common occurrence in satellite precipitation estimation. Our study attests that transferability of model estimators (which help to estimate the error variance) from one region to another is practically possible by leveraging the similarity in geophysical features. Therefore, the quantitative picture of satellite precipitation error over ungauged regions can be discerned even in the absence of ground truth data.

Faisal Hossain

Papers

Paradox of Peak Flows in a Changing Climate

Advancing the Use of Satellite Rainfall Datasets for Flood Prediction in Ungauged Basins: The Role of Scale, Hydrologic Process Controls and the Global Precipitation Measurement Mission

Study of Antioxidative Properties of Some Mono Amino-Acid-Type and Dipeptide-Type Surfactants

Understanding Volumetric Water Storage in Monsoonal Wetlands of Northeastern Bangladesh

How Well Can We Estimate Error Variance of Satellite Precipitation Data Around the World