Improving flood forecasting using multi-source remote sensing data – Report of the Floodfore project

Current remote sensing satellites can provide valuable information relevant to hydrological monitoring. And by using available in situ measurements together with the satellite data the information can be even more valuable. The FloodFore project developed new methods to estimate hydrological parameters from multi source remote sensing and in situ data. These hydrological parameters are important input to the watershed simulation model in order to improve the accuracy of its forecasts. In the project several new methods were either developed or demonstrated: satellite based snow water equivalent (SWE) estimation, weather radar based accumulated precipitation estimation, satellite based soil freezing state determination, and SWE estimation with high spatial resolution using both microwave radiometer and SAR data. Also a visualisation system for multi source information was developed to demonstrate the new products to users. The effect of the snow remote sensing estimates to the hydrological forecasting accuracy was studied for the Kemijoki river basin. The commercialisation possibilities of the results of the project were also studied

ANALYSIS OF THREE DIFFERENT MACHINE LEARNING ALGORITHMS FOR SWE ESTIMATION OVER WESTERN COLORADO USING SPACE-BASED PASSIVE MICROWAVE RADIOMETRY

This study compares the performance of three different machine learning algorithms used for snow water equivalent (SWE) estimation. Inputs to these algorithms include passive microwave (PMW) brightness temperature (Tb) observations at 10.65 GHz, 18.7 GHz, and 36.5 GHz at both vertical and horizontal polarization as collected by the Advanced Microwave Scanning Radiometer (AMSR-2). The three algorithms include: 1) support vector machine (SVM) regression, 2) long short-term memory (LSTM) networks, and 3) Gaussian process (GP) regression. In-situ SWE measurements from the SNOTEL network collected across western Colorado is used as the training “targets” during the training procedure. The performance of the algorithms is evaluated using a number of different metrics including, but not limited to correlation coefficient, mean square error (MSE), and bias. The evaluation is conducted over a range of different elevations and different land cover classifications in order to assess algorithm performance across a broad range of snowpack conditions. Preliminary results suggest the LSTM algorithm is computationally more efficient during the training process as compared to the other algorithms, yet yields a similar level of performance. Some limitations, however, have been found in the study, including poor performance during deep snow conditions, which is likely related to signal “saturation” within the PMW Tb’s used during the supervised training process. Additionally, algorithm performance is strongly dependent on the amount of training data such that too little training data results in poor performance by the algorithm at successfully reproducing inter-annual variability. The strengths and limitations of these different machine learning algorithms for snow mass estimation will be discussed

Alpine sensor network system for high-resolution spatial snow and runoff estimation

Monitoring the snowpack is crucial for water management, flood control and hydropower optimization. Traditional regression methods often result in low accuracy runoff predictions.Existing ground-based real-time measurement systems are in majority installed at low elevations with poor physiographic representation. This thesis presents a system for better Snow Water Equivalent (SWE) and runoff estimation. The autonomous end-to-end Wireless Sensor Network (WSN) that leverages the Internet of Things (IoT) technology provides mountain hydrology measurements in near real-time. At its core lies an ultra-low power, radio channel-hoping, and self-organizing mesh secured with a rugged weather-sealed design, data replication and remote network health monitoring. Three WSNs are installed throughout the North Fork of the Feather River in Northern California upstream of the Oroville dam. Elevation, aspect, slope and vegetation determine network locations. Data show considerable spatial variability of snow depth, and that existing operational autonomous systems are non-representative spatially, with biases reaching up to 50%. Combined with existing systems, WSNs better detect precipitation timing and phase, monitor sub-daily dynamics of infiltration and surface runoff, and inform hydro power managers about actual ablation and end-of-season date across the landscape. A wet and dry year exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Elastic Net regression shows that dominant features at the sub-km2 scale are site-dependent and differ from the watershed scale. Based on the Nearest Neighbor (NN) with a Landsat assimilated historical product, explanatory variables consistently explain up to 90% of the variance in the watershed-scale SWE for both years. Lagged cross correlation of snowmelt with stream flow measurements show improvement of up to 100% compared to existing systems. Ensemble Optimal Interpolation (EnOI) update of background SWE fields from Landsat and LiDAR products provide accurate high resolution estimates of spatial SWE for areas with parsimonious sensors. Results show a minimum RMSE of 22% and 30% at 90 m and 50 m resolutions respectively. Compared with SNODAS, reduction in error is up to 55% and 80%, with LiDAR as reference

Estimating the water budget components and their variability in a pre-alpine basin with JGrass-NewAGE

The estimation of water resources at basin scale requires modelling of all components of the hydrological system. Because of the great uncertainties associated with the estimation of each water cycle component and the large error in budget closure that results, water budget is rarely carried out explicitly. This paper fills the gap in providing a methodology for obtaining it routinely at daily and subdaily time scales. In this study, we use various strategies to improve water budget closure in a small basin of Italian Prealps. The specific objectives are: assessing the predictive performances of different Kriging methods to determine the most accurate precipitation estimates; using MODIS imagery data to assist in the separation of snowfall and rainfall; combining the Priestley-Taylor evapotranspiration model with the Budyko hypothesis to estimate at high resolution (in time and space) actual evapotranspiration (ET); using an appropriate calibration-validation strategy to forecast discharge spatially. For this, 18 years of spatial time series of precipitation, snow water equivalent, rainfall-runoff and ET at hourly time steps are simulated for the Posina River basin (Northeast Italy) using the JGrass-NewAGE system. Among the interpolation methods considered, local detrended kriging is seen to give the best performances in forecasting precipitation distribution. However, detrended Kriging gives better results in simulating discharges. The parameters optimized at the basin outlet over a five-year period show acceptable performances during the validation period at the outlet and at interior points of the basin. The use of the Budyko hypothesis to guide the ET estimation shows encouraging results, with less uncertainty than the values reported in literature. Aggregating at a long temporal scale, the mean annual water budget for the Posina River basin is about 1269 ± 372 mm (76.4%) runoff, 503.5 ± 35.5 mm (30%) evapotranspiration, and −50±129−50±129 mm (−−4.2%) basin storage from basin precipitation of 1730 ± 344 mm. The highest interannual variability is shown for precipitation, followed by discharge. Evapotranspiration shows less interannual variability and is less dependent on precipitation

Snow Properties Retrieval Using Passive Microwave Observations

Seasonal snow cover, the second-largest component of the cryosphere, is crucial in controlling the climate system, through its important role in modifying Earth’s albedo. The temporal variability of snow extent and its physical properties in the seasonal cycle also make up a significant element to the cryospheric energy balance. Thus, seasonal snowcover should be monitored not only for its climatological impacts but also for its rolein the surface-water supply, ground-water recharge, and its insolation properties at local scales. Snowpack physical properties strongly influence the emissions from the substratum, making feasible snow property retrieval by means of the surface brightness temperature observed by passive microwave sensors. Depending on the observing spatial resolution, the time series records of daily snow coverage and a snowpacks most-critical properties such as the snow depth and snow water equivalent (SWE) could be helpful in applications ranging from modeling snow variations in a small catchment to global climatologic studies. However, the challenge of including spaceborne snow water equivalent (SWE) products in operational hydrological and hydroclimate modeling applications is very demanding with limited uptake by these systems. Various causes have been attributed to this lack of up-take but most stem from insufficient SWE accuracy. The root causes of this challenge includes the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process that are caused by uncertainties with the forward emission modeling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of the whole range of retrieval methodologies can provide the clarity needed to move the thinking forward in this important field. Following a review on snow depth and SWE retrieval methods using passive microwave remote sensing observations, this research employs a forward emission model to simulate snowpacks emission and compare the results to the PM airborne observations. Airborne radiometer observations coordinated with ground-based in-situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to the volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this effect has yet to be confirmed. Therefore, the Dense Media Radiative Transfer Theory forMulti Layered (DMRT-ML) snowpack is used to predict the passive microwave response from airborne observations over shallow, dense, slab-layered tundra snow. The DMRT-ML was parameterized with the in-situ snow measurements using a two-layer snowpack and run in two configurations: a depth hoar and a wind slab dominated pack. Snow depth retrieval from passive microwave observations without a-priori information is a highly underdetermined system. An accurate estimate of snow depth necessitates a-priori information of snowpack properties, such as grain size, density, physical temperature and stratigraphy, and, very importantly, a minimization of this a prior information requirement. In previous studies, a Bayesian Algorithm for Snow Water Equivalent (SWE) Estimation (BASE) have been developed, which uses the Monte Carlo Markov Chain (MCMC) method to estimate SWE for taiga and alpine snow from 4-frequency ground-based radiometer Tb. In our study, BASE is used in tundra snow for datasets of 464 footprints inthe Eureka region coupled with airborne passive microwave observations—the same fieldstudy that forward modelling was evaluated. The algorithm searches optimum posterior probability distribution of snow properties using a cost function between physically based emission simulations and Tb observations. A two-layer snowpack based on local snow cover knowledge is assumed to simulate emission using the Dense Media Radiative Transfer-Multi Layered (DMRT-ML) model. Overall, the results of this thesis reinforce the applicability of a physics-based emission model in SWE retrievals. This research highlights the necessity to consider the two-part emission characteristics of a slab-dominated tundra snowpack and suggests performing inversion in a Bayesian framework

Using SAR data for wet snow monitoring

Zjišťování mokrého sněhu z radarových dat Abstrakt Tato práce se zaměřuje na existující metodu pro získávání informací o sněhové pokrývce z družicových radarových dat. Zkoumaná metoda byla navržena Malnesem a Guneriussenem (2002) a je schopná provést subpixelovou klasifikaci mokrého sněhu, a také klasifikovat pixely se suchým sněhem. Klasifikace je založená na detekci změn, takže je potřeba referenční snímek bez sněhové pokrývky. V průběhu zpracování byly v algoritmu objeveny některé nedostatky, které jsou v práci diskutovány, a zároveň je navrženo možné řešení. Navrhnul jsem také modifikaci tohoto algoritmu, která by mohla přispět ke zlepšení jeho přesnosti. Modifikovaný algoritmus jsem pak otestoval. Klíčová slova: SAR, sněhová pokrývka, dálkový průzkum Země, mokrý sníhUsing SAR data for wet snow monitoring Abstract This paper focuses on an existing method of snow information retrieval by means of satellite SAR data. The method was first presented by Malnes and Guneriussen (2002), and has been proven to be capable of sub-pixel classification of wet snow. It is also able to classify dry snow pixels. The classification is based on change detection, so a snow-free reference image is required. Some flaws in this algorithm have been discovered during the work on this paper and are discussed, as well as a possible solution is suggested. I have also proposed a modification of the algorithm which could improve the classification results and tested the modified algorithm. Keywords: SAR, snow cover, remote sensing, wet snowDepartment of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Development and evaluation of an advanced microwave radiance data assimilation system for estimating snow water storage at the continental scale

Snow cover modulates the Earth's surface energy and water fluxes, and snowmelt runoff is the principal source of water for humans and ecosystems in many of the middle to high latitudes in the Northern Hemisphere. Understanding spatial and temporal variation in snowpack is crucial for climate studies and water resource management and thus the climate and hydrological research communities have invested in improving large-scale snow estimates. This dissertation aims to develop an advanced snow radiance assimilation (RA) system to improve continental-scale snow water storage estimates. The RA system is comprised of the Community Land Model version 4 (CLM4) (for snow energy and mass balance modeling), radiative transfer models (RTMs) (for brightness temperature estimates), and the Data Assimilation Research Testbed (DART) (for ensemble-based data assimilation). Two snowpack RTMs, the Microwave Emission Model for Layered Snowpacks (MEMLS) and the Dense Media Radiative Transfer--Multi Layers model (DMRT-ML), are used to simulate T[subscript B] of a multi-layered snowpack. Through an error characterization study, this dissertation presents that the correlations between snow water equivalent (SWE) error and brightness temperature (T[subscript B]) error and subsequent RA performance in estimating snow are significantly affected by all physical properties of soil and snow involved in estimating T[subscript B]. Based on the error characterization results, it is hypothesized that the continental-scale RA performance in estimating snow water storage can be improved by simultaneously updating all model physical states and parameters determining T[subscript B] based on a rule, in which prior estimates are updated depending on their correlations with a prior T[subscript B]. The results of a series of RA experiments show that the improved continental-scale snow estimates are obtained by applying the hypothesis. This dissertation also shows that further improvement of the performance of the RA system can be achieved, especially for vegetated areas, by assimilating the best-performing frequency channels (i.e., 18.7 and 23.8 GHz) and by considering the vegetation single scattering albedo to represent the vegetation effect on T[subscript B] at the top of the atmosphere.Geological Science