Estimation of tail dependence coefficient in rainfall accumulation fields

Extreme rainfall events are of particular importance due to their severe impacts on the economy, the environment and the society. Characterization and quantification of extremes and their spatial dependence structure may lead to a better understanding of extreme events. An important concept in statistical modeling is the tail dependence coefficient (TDC) that describes the degree of association between concurrent rainfall extremes at different locations. Accurate knowledge of the spatial characteristics of the TDC can help improve on the existing models of the occurrence probability of extreme storms. In this study, efficient estimation of the TDC in rainfall is investigated using a dense network of rain gauges located in south Louisiana, USA. The inter-gauge distances in this network range from about 1 km to 9 km. Four different nonparametric TDC estimators are implemented on samples of the rain gauge data and their advantages and disadvantages are discussed. Three averaging time-scales are considered: 1 h, 2 h and 3 h. The results indicate that a significant tail dependency may exist that cannot be ignored for realistic modeling of multivariate rainfall fields. Presence of a strong dependence among extremes contradicts with the assumption of joint normality, commonly used in hydrologic applications.     

Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula

Quantification of rainfall and its spatial and temporal variability is extremely important for reliable hydrological and meteorological modeling. While rain gauge measurements do not provide reasonable areal representation of rainfall, remotely sensed precipitation estimates offer much higher spatial resolution. However, uncertainties associated with remotely sensed rainfall estimates are not well quantified. This issue is important considering the fact that uncertainties in input rainfall are the main sources of error in hydrologic processes. Using an ensemble of rainfall estimates that resembles multiple realizations of possible true rainfall, one can assess uncertainties associated with remotely sensed rainfall data. In this paper, ensembles are generated by imposing rainfall error fields over remotely sensed rainfall estimates. A non-Gaussian copula-based model is introduced for simulation of rainfall error fields. The v-transformed copula is employed to describe the dependence structure of rainfall error estimates without the influence of the marginal distribution. Simulations using this model can be performed unconditionally or conditioned on ground reference measurements such that rain gauge data are honored at their locations. The presented model is implemented for simulation of rainfall ensembles across the Little Washita watershed, Oklahoma. The results indicate that the model generates rainfall fields with similar spatio-temporal characteristics and stochastic properties to those of observed rainfall data.     

Extraction and accuracy assessment of high-resolution DEM and derived orthoimages from ALOS-PRISM data over Sahel-Doukkala (Morocco)

In Sahel-Doukkala, which is characterized by lands of a relatively low relief, global DEMs and DEMs generated from digitizing topographic maps, have been the primary source of several multidisciplinary researches. Although these products present a great value of the conducted research, the level of the given accuracy is not sufficient enough for detailed geospatial analysis. These requirements led us to generate a high-resolution DEM as an alternative of available global DEMs or/and DEMs generated from digitizing topographic maps. In this study, we present a workflow to extract high-resolution DEM at 5 m resolution and derived orthoimages from ALOS-PRISM data over Sahel-Doukkala, through photogrammetric techniques, using a variation of GCPs obtained from topographic maps at scale 1:25,000. The accuracy of the generated products is reported according to NSSDA standards. Using ten GCPs, a PRISM-DEM with 3.88 m vertical accuracy and 11.60 m horizontal accuracy, both at 95% confidence level is obtained. This DEM will serve as base dataset for further detailed geospatial analysis and mapping applications in order to identify the relationship between surface parameters and groundwater, and also to assess and understand all factors influencing the development of karst landscapes and consequently subsurface stability in the investigated area.     

Observational characteristics of cloud radiative effects over three arid regions in the Northern Hemisphere

Cloud–radiation processes play an important role in regional energy budgets and surface temperature changes over arid regions. Cloud radiative effects (CREs) are used to quantitatively measure the aforementioned climatic role. This study investigates the characteristics of CREs and their temporal variations over three arid regions in central Asia (CA), East Asia (EA), and North America (NA), based on recent satellite datasets. Our results show that the annual mean shortwave (SW) and net CREs (SWCRE and NCRE) over the three arid regions are weaker than those in the same latitudinal zone of the Northern Hemisphere. In most cold months (November–March), the longwave (LW) CRE is stronger than the SWCRE over the three arid regions, leading to a positive NCRE and radiative warming in the regional atmosphere–land surface system. The cold-season mean NCRE at the top of the atmosphere (TOA) averaged over EA is 4.1 W m^–2, with a positive NCRE from November to March, and the intensity and duration of the positive NCRE is larger than that over CA and NA. The CREs over the arid regions of EA exhibit remarkable annual cycles due to the influence of the monsoon in the south. The TOA LWCRE over arid regions is closely related to the high-cloud fraction, and the SWCRE relates well to the total cloud fraction. In addition, the relationship between the SWCRE and the low-cloud fraction is good over NA because of the considerable occurrence of low cloud. Further results show that the interannual variation of TOA CREs is small over the arid regions of CA and EA, but their surface LWCREs show certain decreasing trends that correspond well to their decreasing total cloud fraction. It is suggested that combined studies of more observational cloud properties and meteorological elements are needed for indepth understanding of cloud–radiation processes over arid regions of the Northern Hemisphere.