Managing climate risk in water supply systems : materials and tools designed to empower technical professionals to better understand key issues

This manual has been developed as a learning tool to be used with a companion series of practical exercises. They have been developed to provide a hands-on approach to learning key concepts in hydrology and climate science as they relate to climate risk management in water supply systems, as introduced in the text

A review of spatial downscaling of satellite remotely sensed soil moisture

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed

Engaging the user community for advancing societal applications of the surface water ocean topography mission

Scheduled for launch in 2021, the Surface Water and Ocean Topography (SWOT) mission will be a truly unique mission that will provide high-temporal-frequency maps of surface water extents and elevation variations of global water bodies (lakes/reservoirs, rivers, estuaries, oceans, and sea ice) at higher spatial resolution than is available with current technologies (Biancamaria et al. 2016; Alsdorf et al. 2007). The primary instrument on SWOT is based on a Ka-band radar interferometer (KaRIN), which uses radar interferometery technology. The satellite will fly two radar antennas at either end of a 10-m (33 ft) mast, allowing it to measure the elevation of the surface along a 120-km (75 mi)-wide swath below. The availability of high-frequency and high-resolution maps of elevations and extents for surface water bodies and oceans will present unique opportunities to address numerous societally relevant challenges around the globe (Srinivasan et al. 2015). These opportunities may include such diverse and far-ranging applications as fisheries management, flood inundation mapping/risk mitigation/forecasting, wildlife conservation, global data assimilation for improving forecast of ocean tides and weather, reservoir management, climate change impacts and adaptation, and river discharge estimation, among others

A synthetic model to downscale and forecast evapotranspiration using wavelets and SVMs

Provision of reliable forecasts of evapotranspiration (ET) at the farm level can be a key element in efficient water management in irrigated basins. This paper presents an algorithm that provides a means to downscale and forecast ET images. The key concepts driving the development of this algorithm are building multiple relationships between inputs and outputs at all different spatial scales, and using these relationships to downscale and forecast the output at the finest scale. This downscaling/forecasting algorithm is designed for dependent properties such as ET. Decomposing and reconstructing processes are done using two-dimensional (2D) discrete wavelet decomposition (2D- DWT) with basis functions that suit the physics of the property in question. 2D- DWT, for one level, results in one datum image (Low-Low pass filter image, or LL) and three detailing images (Low-High or LH, High-Low or HL, and HighHigh or HH). The underlying physics between the input variables and the output are learned by using Support Vector Machines (SVMs) at the resolution of the output. The machines are then applied at a higher resolution to produce detailing images to help downscale the output image (e.g., ET). In addition to being downscaled, the output image can be shifted ahead in time, providing a means for the algorithm to be used for forecasting. The algorithm has been applied on two case studies, one in Bondville, Illinois where the results have been validated against Ameriflux observations, and another in the Sevier River Basin, Utah

Problems Related to Missing Data in Hydrologic Modeling: Implications and Solution

A common practice in pre-processing data for hydrological modeling is to ignore observations with any missing variable values at any given time step, even if it is only one of the independent variables that is missing. These rows of data are labeled incomplete and would not be used in either model building or subsequent testing and verification steps. This is not necessarily the best way of doing it as information is lost when incomplete rows of data are discarded. Learning algorithms are affected by such problems more than physically-based models as they rely heavily on the data to learn the underlying input/output relationships. In this study, the extent of damage to the performance of the learning algorithm due to missing data is explored in a field-scale application. We have tested and compared the performance of two well-known learning algorithms, namely Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for short-term prediction of groundwater levels in a well field. A comparison of these two algorithms is made using various percentages of missing data. In addition to understanding the relative performance of these algorithms in dealing with missing data, a solution in the form of an imputation methodology is proposed for filling the data gaps. The proposed imputation methodology is tested against observed data

A new Bayesian recursive technique for parameter estimation

The performance of any model depends on how well its associated parameters are estimated. In the current application, a localized Bayesian recursive estimation (LOBARE) approach is devised for parameter estimation. The LOBARE methodology is an extension of the Bayesian recursive estimation (BARE) method. It is applied in this paper on two different types of models: an artificial intelligence (AI) model in the form of a support vector machine (SVM) application for forecasting soil moisture and a conceptual rainfall-runoff (CRR) model represented by the Sacramento soil moisture accounting (SAC-SMA) model. Support vector machines, based on statistical learning theory (SLT), represent the modeling task as a quadratic optimization problem and have already been used in various applications in hydrology. They require estimation of three parameters. SAC-SMA is a very well known model that estimates runoff. It has a 13-dimensional parameter space. In the LOBARE approach presented here, Bayesian inference is used in an iterative fashion to estimate the parameter space that will most likely enclose a best parameter set. This is done by narrowing the sampling space through updating the “parent” bounds based on their fitness. These bounds are actually the parameter sets that were selected by BARE runs on subspaces of the initial parameter space. The new approach results in faster convergence toward the optimal parameter set using minimum training/calibration data and fewer sets of parameter values. The efficacy of the localized methodology is also compared with the previously used BARE algorithm

Multiobjective particle swarm optimization for parameter estimation in hydrology

Modeling of complex hydrologic processes has resulted in models that themselves exhibit a high degree of complexity and that require the determination of various parameters through calibration. In the current application we introduce a relatively new global optimization tool, called particle swarm optimization (PSO), that has already been applied in various other fields and has been reported to show effective and efficient performance. The PSO approach initially dealt with a single-objective function but has been extended to deal with multiobjectives in a form called multiobjective particle swarm optimization (MOPSO). The algorithm is modified to account for multiobjective problems by introducing the Pareto rank concept. The new MOPSO algorithm is tested on three case studies. Two test functions are used as the first case study to generate the true Pareto fronts. The approach is further tested for parameter estimation of a well-known conceptual rainfall-runoff model, the Sacramento soil moisture accounting model having 13 parameters, for which the results are very encouraging. We also tested the MOPSO algorithm to calibrate a three-parameter support vector machine model for soil moisture prediction