Managing Climate Risk in Water Supply Systems

Using decadal prediction experiments from the WCRP/CMIP5 suite that were initialized every year from 1960-onward, we explore long-lead predictability of ENSO events. Both deterministic and probabilistic skill metrics are used to assess the ability of these decadal prediction systems to reproduce ENSO variability as represented by the NINO3.4 index (EN3.4). Several individual systems as well as the multi-model mean can predict ENSO events 3–4 years in advance, though not for every event during the hindcast period. This long-lead skill is beyond the previously documented predictability limits of initialized prediction systems. As part of the analysis, skill in reproducing the annual cycle of EN3.4, and the annual cycle of its interannual variability is examined. Most of the prediction systems reproduce the seasonal cycle of EN3.4, but are less able to capture the timing and magnitude of the variability. However, for the prediction systems used here, the fidelity of annual cycle characteristics does not appear to be related to the system’s ability to predict ENSO events. In addition, the performance of the multi-model ensemble mean is explored and compared to the multi-model mean based solely on the most skillful systems; the latter is found to yield better results for the deterministic metrics. Finally, an analysis of the near-surface temperature and precipitation teleconnections reveals that the ability of the systems to detect ENSO events far in advance could translate into predictive skill over land for several lead years, though with reduced amplitudes compared to observations

International Research Institute for Climate and Society

This report documents the process and results of the index insurance design effort leading to the index insurance contracts for Adi Ha in 2009. This report represents deliverables 1 and 2 in the terms of reference with Oxfam America, it outlines and compares analysis and design methodologies including the performance of rainfall simulators for index-based contract design. It also details contracts, methodologies, associated issues, and important lessons learned. A separate project report details the Experimental Games, deliverable 3 in the terms of reference. IRI has delivered contracts and methodology with a significant portion of the effort provided without cost to Oxfam America. This includes contributions from several external researchers who have contributed time at no cost. Substantial funders who have provided additional sponsorship include NOAA, NSF, (through its CRED DMUU center at Columbia), the Columbia Earth Institute postdoctoral program, the Columbia University Lamont postdoctoral program, and an Earth Institute CGSD seed grant. The insurance effort is one component of the Oxfam HARITA project, with the insurance designed to target key gaps left in the HARITA holistic climate risk management portfolio. Because this report focuses on the insurance contract design, it does not provide a comprehensive overview of the full climate risk management strategy, although it does refer to particular components in discussion of design issues. For a complete overview of the risk management and adaptation portfolio, the reader should refer to Oxfam HARITA project documentation. Many partners have participated substantially in the contract design effort. Major partners in this project include the Relief Society of Tigray (REST), Dedebit Credit and Savings Institution (DECSI), Nyala Insurance Company, the Ethiopian Productive Safety Net Program (PSNP), the Government of Ethiopian National Meteorological Agency (ENMA), Swiss Re, Mekele University, Oxfam Horn of Africa Regional Office, and Oxfam America (project coordinator). Any successes of this project depend strongly on the outstanding effort, skill, active engagement, and level of insights from the partners. Crop modeling parameters were obtained from LEAP and Mekele University. This project benefits strongly from groundwork and ongoing index insurance projects in Ethiopia and elsewhere by WFP and the World Bank CRMG

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 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

Downscaling and Assimilation of Surface Soil Moisture Using Ground Truth Measurements

Methods for reconciliation of spatial and temporal scales of data have become increasingly important as remote sensing data become more readily available and as the science of hydrology moves more heavily toward distributed modeling. The purpose of this paper is to develop a method to disaggregate coarse-resolution remote sensing data to finer scale resolutions that are more appropriate for use in hydrologic studies and water management. This disaggregation is done with the help of point measurements on the ground. The downscaling of remote sensing data is achieved by three main steps: initialization, spatial pattern mimicking, and assimilation. The first two steps are part of the main algorithm, and the last step, assimilation, is included for fine-tuning and to ensure further compatibility between the coarse-scale and fine-scale images. The assimilation step also incorporates the information coming from the point measurements. The approach has been applied and validated by downscaling images for two cases. In the first case, a synthetically generated random field is reproduced at fine and coarse resolutions. The downscaled image has been shown to match the spatial properties of the true image according to the variogram test as well as the magnitude of values according to the various univariate goodness-of-fit measures R2 = 0.91. In the second case, a soil moisture field from the Southern Great Plains (SGP 97) experiments is downscaled from a resolution of 800 m × 800 m to a resolution of 50 m × 50 m

Downscaling and Forecasting of Evapotranspiration Using a Synthetic Model of Wavelets and Support Vector Machines

See all › 27 Citations See all › 60 References See all › 8 FiguresShare Download Full-text PDF Downscaling and Forecasting of Evapotranspiration Using a Synthetic Model of Wavelets and Support Vector Machines Article (PDF Available) in IEEE Transactions on Geoscience and Remote Sensing 46(9):2692 - 2707 · October 2008 with 155 Reads DOI: 10.1109/TGRS.2008.919819 · Source: IEEE Xplore 1st Yasir Kaheil 17.68 · FM Global, Norwood, MA, USA 2nd Enrique Rosero + 1 3rd M.K. Gill Last Luis Bastidas 28.25 · Utah State University Show more authors Abstract Providing reliable forecasts of evapotranspiration (ET) at farm level is a key element toward efficient water management in irrigated basins. This paper presents an algorithm that provides a means to downscale and forecast dependent variables such as ET images. Using the discrete wavelet transform (DWT) and support vector machines (SVMs), the algorithm finds multiple relationships between inputs and outputs at all different spatial scales and uses these relationships to predict the output at the finest resolution. Decomposing and reconstructing processes are done by using 2-D DWT with basis functions that suit the physics of the property in question. Two-dimensional DWT for one level will result in one datum image (low-low-pass filter image) and three detail images (low-high, high-low, and high-high). The underlying relationship between the input variables and the output are learned by training an SVM on the datum images at the resolution of the output. The SVM is then applied on the detailed images to produce the detailed images of the output, which are needed to help downscale the output image to a higher resolution. 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, IL, where the results have been validated against AmeriFlux observations, and another in the Sevier River Basin, UT