Downscaling Coarse Resolution Satellite Passive Microwave SWE Estimates

The spatio-temporal heterogeneity of seasonal snow and its impact on socio-economic and environmental functionality make accurate, real-time estimates of snow water equivalent (SWE) important for hydrological and climatological predictions. Passive microwave remote sensing offers a cost effective, temporally and spatially consistent approach to SWE monitoring at the global to regional scale. However, local scale estimates are subject to large errors given the coarse spatial resolution of passive microwave observations (25 x 25 km). Regression downscaling techniques can be implemented to increase the spatial resolution of gridded datasets with the use of related auxiliary datasets at a finer spatial resolution. These techniques have been successfully implemented to remote sensing datasets such as soil moisture estimates, however, limited work has applied such techniques to snow-related datasets. This thesis focuses on assessing the feasibility of using regression downscaling to increase the spatial resolution of the European Space Agency’s (ESA) Globsnow SWE product in the Red River basin, an agriculturally important region of the northern United States that is widely recognized as a suitable location for passive microwave remote sensing research. Multiple Linear (MLR), Random Forest (RFR) and Geographically Weighted (GWR) regression downscaling techniques were assessed in a closed loop experiment using Snow Data Assimilation System (SNODAS) SWE estimates at a 1 x 1 km spatial resolution. SNODAS SWE data for a 5-year period between 2013-2018 was aggregated to a 25 x 25 km spatial resolution to match Globsnow. The three regression techniques were applied using correlative datasets to downscale the aggregated SNODAS data back to the original 1 x 1 km spatial resolution. By comparing the downscaled SNODAS estimates to the original SNODAS data, it was found that RFR downscaling produced much less variation in downscaled results, and lower RMSE values throughout the study period when compared to MLR and GWR downscaling techniques, indicating it was the optimal downscaling method. RFR downscaling was then implemented on daily Globsnow SWE estimates for the same time period. The downscaled SWE results were evaluated using SNODAS SWE as well as in situ derived SWE estimates from weather stations within the study region. Spatial and temporal errors were assessed using both the SNODAS and in situ reference datasets and overall RMSEs of 21 mm and 37 mm were found, respectively. It was observed that the southern regions of the basin and seasons with higher downscaled SWE estimates were associated with higher errors with overestimation being the most common bias throughout the region. A major contribution of this study is the illustration that RFR downscaling of Globsnow SWE estimates is a feasible approach to understanding the seasonal dynamics of SWE in the Red River basin. This is extremely beneficial for local communities within the basin for flood management and mitigation and water resource management

Drought assessment modelling using biophysical parameters and remote sensing data

This study considers the advancement in technical development of a few disciplines as an infrastructure for developing a suitable model and methodology for agricultural drought assessment in semi-arid area. It evaluates capabilities of multisource remote sensing data in developing raster-based biophysical drought assessment models. The capability for expressing the spatial and inter-annual variation of evapotranspiration (ET) over a study area by the proposed models has made it efficient. The base model, Mapping EvapoTranspiration at high Resolution with Internal Calibration (METRIC) has been evaluated for its performance in estimating ET over the pistachio plantation in a semi-arid region. The result proved that the base model gives good accuracy and is suitable for the selected study area. The base model, METRIC, is found sensitive to a number of meteorological parameters. Two-factor analysis for the primary inputs of the base model shows that the surface albedo and surface temperature pairs is the most effective while other tested pairs are found to be least effective. The study suggests that improving the equations of the effective pair should increase the accuracy. In this case, the multilayer perceptron Artificial Neural Network (ANN) technique is used for estimating spatial and temporal distribution of actual ET from satellite based biophysical parameters. The result shows that a strong correlation exist between ET values computed using METRIC and those generated using ANN. ANN sensitivity analysis shows that surface temperature, soil heat flux and surface albedo are the most significant parameters. Exploratory factor analysis using Principal Component Analysis (PCA) was performed to select the most significant biophysical parameters to be used as input to a newly developed BioPhysical Water Stress Index (BPWSI). The BPWSI is a new model for estimating water stress index using the selected biophysical parameters. The results of BPWSI are found to be significant and can be used for predicting the pistachio water status which represents the indication of agricultural drought

Solució paral·lelitzada d'interpolació kriging amb ajust automatitzat del variograma

El principal objectiu d'aquest treball és proporcionar una metodologia per a reduir el temps de càlcul del mètode d'interpolació kriging sense pèrdua de la qualitat del model resultat. La solució adoptada ha estat la paral·lelització de l'algorisme mitjançant MPI sobre llenguatge C. Prèviament ha estat necessari automatitzar l'ajust del variograma que millor s'adapta a la distribució espacial de la variable d'estudi. Els resultats experimentals demostren la validesa de la solució implementada, en reduir de forma significativa els temps d'execució final de tot el procés.El principal objetivo de este trabajo es proporcionar una metodología para reducir el tiempo de cálculo del método de interpolación kriging sin pérdida de la calidad del modelo resultado. La solución adoptada ha sido la paralelización del algoritmo mediante MPI sobre lenguaje C. Previamente ha sido necesario automatizar el ajuste del variograma que mejor se adapta a la distribución espacial de la variable de estudio. Los resultados experimentales demuestran la validez de la solución implementada, al reducir de forma significativa los tiempos de ejecución finales del proceso completo.The main objective of this work is to provide a methodology to reduce the time needed to calculate the kriging interpolation method without losing any quality of the resulting model. The solution adopted has been the algorithm parallelization by MPI on C language. Previously, it has been necessary to automate the variogram fitting that best suits the spatial distribution of the variable at study. The experimental results demonstrate the validity of the implemented solution, to significantly reduce the execution times of the entire process

Improving Alpine Flood Prediction through Hydrological Process Characterization and Uncertainty Analysis

Among the many challenges of Alpine flood prediction is describing complex, meteo-hydrological processes in a simplified, robust manner that can be easily integrated into operational forecasting. In this dissertation, improved methods to characterize these processes are developed and integrated into the hydrological modeling component of an operational flood forecasting system used in the Swiss Alps. Detailed studies are conducted to improve hydrological model inputs, processes and outputs. Improvements, detailed in four chapters of this thesis, address the overarching goal of this work – the reduction of flood forecasting uncertainty. The accuracy of flood predictions in Alpine areas is contingent upon adequate interpolation of meteorological forcings, which has significant impacts on discharge volumes and flood peaks. This thesis demonstrates an improvement in the interpolation of temperature and precipitation inputs using a robust variogram which considers anisotropy and using a geostatistical interpolation method to distribute inputs in space and time. Results show that using elevation as the external drift factor better describes orographically-induced precipitation and temperature gradients. Also, the consideration of anisotropy is integral in detailing spatial patterns of precipitation induced by storm advection. Hydrological flood forecasting in mountainous areas also requires accurate partitioning between rain and snowfall to properly estimate the extent of runoff contributing areas. Partitioning is improved in this work by using a new method to integrate Limited Area Model output. Unlike standard hydrological procedures in inferring snowfall limit estimates based on dry, ground temperature measurements, Limited Area Model output considers the vertical, humid, atmospheric structure in its snowfall limit calculations. In effect, this method provides good estimates of runoff contributing areas in the spring as evidenced by validation on discharge measurements and satellite images of snow coverage. Accurately describing snowmelt processes on a sub-daily scale is also of critical importance in Alpine flood forecasting. However, the complex topography of the study region has limited observations available for validation. This thesis presents the development of a new physically-based snowmelt method applicable to regions with limited data. This method uses only daily minimum and maximum temperatures to mimic the effects of radiation. A comparative analysis of snowmelt methods is validated with snow lysimeter data and with a unique, distributed meteorological dataset collected by a wireless weather station network. Results demonstrate that the new method is competitive with more complex snowmelt methods as shown by accurately reproducing diurnal snowmelt cycles. Conveying limits of certainty on flood prediction outputs to users is critical because of epistemic and aleatory errors inherent to environmental modeling. Due to the presence of these errors, the GLUE methodology and multi-criteria performance ideas have been adapted with a fit-for-purpose uncertainty estimation technique in the final part of this thesis. With this method, hydrological model parameters are constrained based on hydrograph behavior, with a particular focus on flood peak response. A key component of the technique is a visualization tool which shows acceptable ensembles of discharge with respect to individual and combined criteria. By integrating the aforementioned input and process improvements into the hydrological model, calibration achieves model outputs that capture observed river discharge. Also, the uncertainty associated with hydrological modeling output error is reduced. Findings of this thesis are applicable to operational flood forecasting in general and have proven utility in improving hydrological model predictions in mountainous regions. Due to the novelty of the developments in terms of new methods or the use of tools and data sources previously unexploited in flood forecasting, further testing of the improvements is recommended. Future research in quantifying the chain of uncertainty produced by combining probabilistic forecast inputs with the hydrological output ensembles is also critical when the improved flood forecasting model becomes fully operational