Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (JuneSeptember) as compared to the dry season (NovemberJanuary). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA

Bringing Statistical Learning Machines Together for Hydro-Climatological Predictions - Case Study for Sacramento San Joaquin River Basin, California

Study region: Sacramento San Joaquin River Basin, California Study focus: The study forecasts the streamflow at a regional scale within SSJ river basin with largescale climate variables. The proposed approach eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962–2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors in the non-linear regression models (K-nearest neighbor regression and data-driven support vector machine). New hydrological insights: The SVD results identified new spatial regions that have not been included in existing predefined indices. The nonparametric model indicated the teleconnections of SHUM500 and U500 being better streamflow predictors compared to other climate variables. The regression models were capable to apprehend most of the sustained low flows, proving the model to be effective for drought-affected regions. It was also observed that the proposed approach showed better forecasting skills with preprocessed large scale climate variables rather than using the predefined indices. The proposed study is simple, yet robust in providing qualitative streamflow forecasts that may assist water managers in making policy-related decisions when planning and managing watersheds

A Study on Acute and Transient Psychotic Disorder - Clinical Charactertics and Diagnostic Stability.

INTRODUCTION: Acute and transient psychotic disorders with good outcome are recognized in both ICD(10) (World Health Organization 1992 A)(51) and DSM IV(American Psychiatric Association 1994)(2) as distinct from schizophrenia and affective psychoses. Right from the beginning acute and transient psychotic disorder and its equivalents have occupied an ambivalent position challenging the kraeplinian dichotomy There is a growing empirical evidence to suggest that acute brief psychoses exhibit a distinctive epidemiological characteristics (Susser, E., Wanderling, J. 45) and benign long term course (44). These evidence support that the concept of ATPD as an distinct nosological entity (Mojtabai et al,27) The place of non affective still remains uncertain in spite of these distinctive features. ICD 10 came closest to the historical concepts of non affective acute remitting psychosis. But the duration criteria is so restrictive while the DSM IV classification differs from ICD 10 by not including acute onset as a criterion for classification of brief psychotic disorder and schizophreniform disorder. The concept of ATPD and its sub categories in the current classification system (ICD 10) differs from DSM IV and is a new concept with no similar categories in DSM IV. As there is a paucity of literature regarding the diagnostic validity and the sub classification the present study attempts to assess the diagnostic stability of acute and transient psychotic disorder and study their clinical characteristics

An Evaluation of Soil Moisture Retrievals Using Aircraft and Satellite Passive Microwave Observations during SMEX02

The Soil Moisture Experiments conducted in Iowa in the summer of 2002 (SMEX02) had many remote sensing instruments that were used to study the spatial and temporal variability of soil moisture. The sensors used in this paper (a subset of the suite of sensors) are the AQUA satellite-based AMSR-E (Advanced Microwave Scanning Radiometer- Earth Observing System) and the aircraft-based PSR (Polarimetric Scanning Radiometer). The SMEX02 design focused on the collection of near simultaneous brightness temperature observations from each of these instruments and in situ soil moisture measurements at field- and domain- scale. This methodology provided a basis for a quantitative analysis of the soil moisture remote sensing potential of each instrument using in situ comparisons and retrieved soil moisture estimates through the application of a radiative transfer model. To this end, the two sensors are compared with respect to their estimation of soil moisture

High-resolution Monthly Satellite Precipitation Product over the Conterminous United States

We present a data set that enhanced the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) monthly product 3B43 in its accuracy and spatial resolution. For this, we developed a correction function to improve the accuracy of TRMM 3B43, spatial resolution of ~25 km, by estimating and removing the bias in the satellite data using a ground-based precipitation data set. We observed a strong relationship between the bias and land surface elevation; TRMM 3B43 tends to underestimate the ground-based product at elevations above 1500 m above mean sea level (m.amsl) over the conterminous United States. A relationship was developed between satellite bias and elevation. We then resampled TRMM 3B43 to the Digital Elevation Model (DEM) data set at a spatial resolution of 30 arc second (~1 km on the ground). The produced high-resolution satellite-based data set was corrected using the developed correction function based on the bias-elevation relationship. Assuming that each rain gauge represents an area of ~1 km2, we verified our product against 9,200 rain gauges across the conterminous United States. The new product was compared with the gauges, which have 50, 60, 70, 80, 90, and 100% temporal coverage within the TRMM period of 1998 to 2015. Comparisons between the high-resolution corrected satellite-based data and gauges showed an excellent agreement. The new product captured more detail in the changes in precipitation over the mountainous region than the original TRMM 3B43

Soil Moisture as an Indicator of Weather Extremes

In this paper, we investigate floods and droughts in the Upper Mississippi basin over a 50-year period (1950–1999) using a hydrological model (Variable Infiltration Capacity Model – 3 Layer). Simulations have been carried out between January 1950 and December 1999 at daily time-step and 1/8° spatial resolution for the water budget and at hourly time-step and 1° spatial resolution for the energy balance. This paper will provide valuable insights to the slow response components of the hydrological cycle and its diagnostic/predictive value in the case of floods and droughts. The paper compares the use of the Palmer Drought Severity Index against the anomalies of the third layer soil moisture for characterizing droughts and floods. Wavelet and coherency analysis is performed on the soil moisture, river discharge, precipitation and PDSI time series confirm our hypothesis of a strong relationship between droughts and the third layer soil moisture

A global assessment of the timing of extreme rainfall from TRMM and GPM for improving hydrologic design

The tropical rainfall measuring mission (TRMM) has revolutionized the measurement of precipitation worldwide. However, TRMM significantly underestimates rainfall in deep convection systems, being therefore of little help for the analysis of extreme precipitation depths. This work evaluates the ability of both TRMM and the recently launched global precipitation measurement (GPM) mission to help in the identification of the timing of severe rainfall events. We compare the date of occurrence of the most severe daily rainfall recorded each year by a global rain gauge network with the ones estimated by TRMM. The match rate between the two is found to approach 50%, indicating significant consistency between the two data sources. This figure rises to 60% for GPM, indicating the potential for this new mission to improve the accuracy associated with TRMM. Further efforts are needed in improving the GPM conversion algorithms in order to reduce the bias affecting the estimation of intense depths. The results however show that the timing estimated from GPM can provide a solid basis for an extensive characterization of the spatio-temporal distribution of extreme rainfall in poorly gauged regions of the world

The first example of heterogeneous oxidation of secondary amines by tungstate-exchanged Mg-Al layered double hydroxides: a green protocol

Tungstate exchanged Mg-Al layered double hydroxides a as recyclable heterogenised catalyst along with H2O2 as an oxidant for the oxidation of sec-amines to nitrones is developed for the first time, Reactions proceed at a fast rate in aqueous media in a single step at room temperature in good to excellent yields. The heterogenised catalyst showed higher activity (TOF) over their homogeneous analogues and other heterogeneous catalysts reported so far. The obtained catalysts were well characterised by various instrumental techniques such as FT-IR spectroscopy, thermal analysis (TGA and DTA), powder XRD and chemical analysis. The catalyst can be reused for six cycles with consistent activity and selectivity

Earth Observation and Cloud Computing in Support of Two Sustainable Development Goals for the River Nile Watershed Countries

In September 2015, the members of United Nations adopted the 2030 Agenda for Sustainable Development with universal applicability of 17 Sustainable Development Goals (SDGs) and 169 targets. The SDGs are consequential for the development of the countries in the Nile watershed, which are affected by water scarcity and experiencing rapid urbanization associated with population growth. Earth Observation (EO) has become an important tool to monitor the progress and implementation of specific SDG targets through its wide accessibility and global coverage. In addition, the advancement of algorithms and tools deployed in cloud computing platforms provide an equal opportunity to use EO for developing countries with limited technological capacity. This study applies EO and cloud computing in support of the SDG 6 “clean water and sanitation” and SDG 11 “sustainable cities and communities” in the seven Nile watershed countries through investigations of EO data related to indicators of water stress (Indicator 6.4.2) and urbanization and living conditions (Indicators 11.3.1 and 11.1.1), respectively. Multiple approaches including harmonic, time series and correlational analysis are used to assess and evaluate these indicators. In addition, a contemporary deep-learning classifier, fully convolution neural networks (FCNN), was trained to classify the percentage of impervious surface areas. The results show the spatial and temporal water recharge pattern among different regions in the Nile watershed, as well as the urbanization in selected cities of the region. It is noted that the classifier trained from the developed countries (i.e., the United States) is effective in identifying modern communities yet limited in monitoring rural and slum regions