Mohammad J. Tourian

Already up? using mobile phones to track & share sleep behavior

Users share a lot of personal information with friends, family members, and colleagues via social networks. Surprisingly, some users choose to share their sleeping patterns, perhaps both for awareness as well as a sense of connection to others. Indeed, sharing basic sleep data, whether a person has gone to bed or waking up, informs others about not just ones sleeping routines but also indicates physical state, and reflects a sense of wellness. We present Somnometer, a social alarm clock for mobile phones that helps users to capture and share their sleep patterns. While the sleep rating is obtained from explicit user input, the sleep duration is estimated based on monitoring a users interactions with the app. Observing that many individuals currently utilize their mobile phone as an alarm clock revealed behavioral patterns that we were able to leverage when designing the app. We assess whether it is possible to reliably monitor ones sleep duration using such apps. We further investigate whether providing users with the ability to track their sleep behavior over a long time period can empower them to engage in healthier sleep habits. We hypothesize that sharing sleep information with social networks impacts awareness and connectedness among friends. The result from a controlled study reveals that it is feasible to monitor a users sleep duration based just on her interactions with an alarm clock app on the mobile phone. The results from both an in-the-wild study and a controlled experiment suggest that providing a way for users to track their sleep behaviors increased user awareness of sleep patterns and induced healthier habits. However, we also found that, given the current broadcast nature of existing social networks, users were concerned with sharing their sleep patterns indiscriminately.

Characterization of runoff‐storage relationships by satellite gravimetry and remote sensing

GRACE observations of the time-dependent gravity field provide a direct measurement of the monthly state of mass and thus monthly total water storage in a catchment. This for the first time allows for a direct comparison of monthly runoff and water storage. Investigations of global scale Runoff-Storage (R-S) relationships for different climatic conditions show distinct periodic characteristics with hysteresis for total water storage. For fully humid tropical catchments, hysteresis reveals a time invariant temporal delay from storage to runoff. Our spectral analysis supports the fact that the R-S relationships can be characterized as a Linear Time Invariant (LTI) System. As a consequence in time domain an adjustment of time lag leads to correlation of 0.98 between runoff and storage. Based hereon, the hypothesis of a R-S relationship characterized by the superposition of linear contributions from coupled/liquid storage and nonlinear contributions from uncoupled storages is investigated by means of remote sensing. For boreal catchments MODIS snow coverage is used to separate total storage into coupled/liquid and uncoupled/solid components either directly by assigning frozen solid storage to the snow-covered areas or indirectly by a model-based aggregation of snow and liquid according to snow coverage. Both methods show that the nonlinear part of the R-S relationship can be fully assigned to the uncoupled/solid storage while the relationship of runoff and liquid storage can also be characterized as an LTI system. This system behavior thus allows for a direct determination of river runoff from GRACE mass and vice versa for unmanaged catchments, provided that the coupled/uncoupled storage components can be quantified remote sensing.

Large-Scale Runoff from Landmasses: A Global Assessment of the Closure of the Hydrological and Atmospheric Water Balances*

AbstractThe performance of hydrological and hydrometeorological water-balance-based methods to estimate monthly runoff is analyzed. Such an analysis also allows for the examination of the closure of water budgets at different spatial (continental and catchment) and temporal (monthly, seasonal, and annual) scales. For this analysis, different combinations of gridded observations [Global Precipitation Climatology Centre (GPCC), Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC), Climatic Research Unit (CRU), and University of Delaware (DEL)], atmospheric reanalysis models [Interim ECMWF Re-Analysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA)], partially model-based datasets [Global Land Surface Evaporation: The Amsterdam Methodology (GLEAM), Moderate Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration Project (MOD16), and FLUXNET Multi-Tree Ensemble (FLUXNET MTE)], and G...

Estimating Runoff Using Hydro-Geodetic Approaches

Given the continuous decline in global runoff data availability over the past decades, alternative approaches for runoff determination are gaining importance. When aiming for global scale runoff at a sufficient temporal resolution and with homogeneous accuracy, the choice to use spaceborne sensors is only a logical step. In this respect, we take water storage changes from Gravity Recovery And Climate Explorer (grace) results and water level measurements from satellite altimetry, and present a comprehensive assessment of five different approaches for river runoff estimation: hydrological balance equation, hydro-meteorological balance equation, satellite altimetry with quantile function-based stage–discharge relationships, a rudimentary instantaneous runoff–precipitation relationship, and a runoff–storage relationship that takes time lag into account. As a common property, these approaches do not rely on hydrological modeling; they are either purely data driven or make additional use of atmospheric reanalyses. Further, these methods, except runoff–precipitation ratio, use geodetic observables as one of their inputs and, therefore, they are termed hydro-geodetic approaches. The runoff prediction skill of these approaches is validated against in situ runoff and compared to hydrological model predictions. Our results show that catchment-specific methods (altimetry and runoff–storage relationship) clearly outperform the global methods (hydrological and hydro-meteorological approaches) in the six study regions we considered. The global methods have the potential to provide runoff over all landmasses, which implies gauged and ungauged basins alike, but are still limited due to inconsistencies in the global hydrological and hydro-meteorological datasets that they use.

Spatiotemporal densification of river water level time series by multimission satellite altimetry

Limitations of satellite radar altimetry for operational hydrology include its spatial and temporal sampling as well as measurement problems caused by local topography and heterogeneity of the reflecting surface. In this study, we develop an approach that eliminates most of these limitations to produce an approximately 3 day temporal resolution water level time series from the original typically (sub)monthly data sets for the Po River in detail, and for Congo, Mississippi, and Danube Rivers. We follow a geodetic approach by which, after estimating and removing intersatellite biases, all virtual stations of several satellite altimeters are connected hydraulically and statistically to produce water level time series at any location along the river. We test different data-selection strategies and validate our method against the extensive available in situ data over the Po River, resulting in an average correlation of 0.7, Root-Mean-Square Error of 0.8 m, bias of −0.4 m, and Nash-Sutcliffe Efficiency coefficient of 0.5. We validate the transferability of our method by applying it to the Congo, Mississippi, and Danube Rivers, which have very different geomorphological and climatic conditions. The methodology yields correlations above 0.75 and Nash-Sutcliffe coefficients of 0.84 (Congo), 0.34 (Mississippi), and 0.35 (Danube).

Basin‐scale runoff prediction: An Ensemble Kalman Filter framework based on global hydrometeorological data sets

In order to cope with the steady decline of the number of in situ gauges worldwide, there is a growing need for alternative methods to estimate runoff. We present an Ensemble Kalman Filter based approach that allows us to conclude on runoff for poorly or irregularly gauged basins. The approach focuses on the application of publicly available global hydrometeorological datasets for precipitation (gpcc, gpcp, cru, udel), evapotranspiration (modis, fluxnet, gleam, era interim, gldas), and water storage changes (grace, wghm, gldas, merra land). Furthermore, runoff data from the grdc and satellite altimetry derived estimates are used. We follow a least-squares prediction that exploits the joint temporal and spatial auto- and cross-covariance structures of precipitation, evapotranspiration, water storage changes and runoff. We further consider time-dependent uncertainty estimates derived from all datasets. Our in-depth analysis comprises of 29 large river basins of different climate regions, with which runoff is predicted for a subset of 16 basins. Six configurations are analyzed: the Ensemble Kalman Filter (Smoother) and the hard (soft) Constrained Ensemble Kalman Filter (Smoother). Comparing the predictions to observed monthly runoff shows correlations larger than 0.5, percentage biases lower than ± 20%, and nse-values larger than 0.5. A modified nse-metric, stressing the difference to the mean annual cycle, shows an improvement of runoff predictions for 14 of the 16 basins. The proposed method is able to provide runoff estimates for nearly 100 poorly gauged basins covering an area of more than 11,500,000 km2 with a freshwater discharge, in volume, of more than 125,000 m3/s. This article is protected by copyright. All rights reserved.

River discharge estimation using channel width from satellite imagery

Availability of simultaneous in situ river discharge measurements and satellite image acquisitions is the biggest restriction of remote sensing based river discharge estimation methods. In this paper, we propose a width-discharge method for river discharge estimation, that does not need coinciding observations. The method constructs the rating curve through the quantile functions of measured discharge and calculated river width. Because the time of measurement does not play a role in quantile function of time series, there is no need for simultaneous data. The method is employed for Niger River in Africa. Our validation results show that the error in discharge estimation (10 % root mean squared error) is at the same level as the conventional method.

The Inflection-Point Retracking Algorithm: Improved Jason-2 Sea Surface Heights in the Strait of Hormuz

ABSTRACT In this study, a waveform retracking algorithm based on finding the inflection-point of the waveform is proposed. After two-steps pre-processing procedure, we employ this method for 145 cycles of Jason-2 data for two tracks 81 and 16 over the Strait of Hormuz. Moreover, we obtain the corrected SSH by the common empirical methods namely Offset Centre of Gravity, Beta and Threshold as well as the ALES. We compare the SSH time series from proposed algorithm with those from common empirical methods. Results are validated against three nearby tide-gauges in the case study. The correlation coefficient and RMSE between the corrected SSH and tide-gages data were computed for three distance classes from the coastline: 0∼5, 5∼10 and 10∼15 kilometer. Our method improves the averaged RMSE of raw SSH up to 41%, 41% and 24%, for these classes over track 81 and 51%, 38% and 41% over track 16, respectively. The averaged correlation values of the proposed method indicate 33%, 11% and 2% improvement over track 81 and are 29%, 14% and 3% over track 16 for three distance groups, respectively. Our method leads to slightly better results than the successful ALES method, especially within the range of 0∼5 km.

Quantifying the impacts of human water use and climate variationson recent drying of Lake Urmia basin: the value of different sets ofspaceborne and in-situ data for calibrating a hydrological model

During the last decades, the endorheic Lake Urmia basin in northwestern Iran has suffered from declining groundwater tables and a very strong recent reduction in the volume of Lake Urmia. For the case of Lake Urmia basin, this study explores the value of different locally and globally available observation data for adjusting a global hydrological model such that it can be used for distinguishing the impacts of human water use and climate variations. The WaterGAP Global Hydrology Model (WGHM) was for the first time calibrated against multiple in situ and spaceborne data to analyze the decreasing lake water volume, lake river inflow, loss of groundwater, and total water storage in the entire basin during 2003–2013. The calibration process was done using an automated approach including a genetic algorithm (GA) and non-dominated sorting genetic algorithm II (NSGA-II). Then the best-performing calibrated models were run with and without considering water use to quantify the impact of human water use. Observations encompass remote-sensing-based time series of annual irrigated areas in the basin from MODIS, monthly total water storage anomaly (TWSA) from GRACE satellites, and monthly lake volume anomalies. In situ observations include time series of annual inflow into the lake and basin averages of groundwater level variations based on 284 wells. In addition, local estimates of sectoral water withdrawals in 2009 and return flow fractions were utilized. Calibration against MODIS and GRACE data alone improved simulated inflow into Lake Urmia but inflow and lake volume loss were still overestimated, while groundwater loss was underestimated and seasonality of groundwater storage was shifted as compared to observations. Lake and groundwater dynamics could only be simulated well if calibration against groundwater levels led to an adjustment of the fractions of human water use from groundwater and surface water. Thus, in some basins, globally available satellite-derived observations may not suffice for improving the simulation of human water use. According to WGHM simulations with 18 optimal parameter sets, human water use was the reason for 52 %–57 % of the total basin water loss of about 10 km3 during 2003–2013, for 39 %–43 % of the Lake Urmia water loss of about 8 km3, and for up to 87 %–90 % of the groundwater loss. Lake inflow was 39 %–45 % less than it would have been without human water use. The study shows that even without human water use Lake Urmia would not have recovered from the significant loss of lake water volume caused by the drought year 2008. These findings can support water management in the basin and more specifically Lake Urmia restoration plans. Published by Copernicus Publications on behalf of the European Geosciences Union. 1940 S.-M. Hosseini-Moghari et al.: Causes of recent drying of Lake Urmia: a hydrological modeling approach

Least-Squares Prediction of Runoff Over Ungauged Basins

One of the major concerns of hydrology is to quantify the hydrological cycle of basins e.g. by means of modeling the hydrological interactions. However, current hydrological models are far from perfect. The main challenge of modeling is the poor spatio-temporal coverage of in situ databases, which are declining steadily over the past few decades. Among the hydrological interactions, river runoff is of great importance, as it represents a catchment’s behaviour. In order to deal with the growing lack of in situ runoff data, we estimate river runoff of ungauged basins by least-squares prediction. In this method, runoff is predicted by mapping the runoff characteristics of gauged basins into ungauged ones through statistical correlations of past data. We follow two scenarios to form the covariance matrices out of available past in situ river runoff: (1) at the signal level, and (2) at the residual level after subtracting monthly mean values. Our validation shows that both scenarios are able to capture runoff values with relative errors less than 15 % for 80 % of the 25 catchments under study. We obtain Nash-Sutcliffe coefficients of over 0.4 for about 90 % and of over 0.75 for about 50 % of the catchments under study. We are thus able to avoid the complexity of hydrological modeling and the challenges (e.g. uncertainty) of spaceborne approaches for runoff estimation over ungauged basins.