Statistics for Spatial Data.

There is now a large published literature on the strengths and weaknesses of downscaling methods for different climatic variables, in different regions and seasons. However, little attention is given to the choice of downscaling method when examining the impacts of climate change on hydrological systems. This review paper assesses the current downscaling literature, examining new developments in the downscaling field specifically for hydrological impacts. Sections focus on the downscaling concept; new methods; comparative methodological studies; the modelling of extremes; and the application to hydrological impacts.

Consideration is then given to new developments in climate scenario construction which may offer the most potential for advancement within the ‘downscaling for hydrological impacts’ community, such as probabilistic modelling, pattern scaling and downscaling of multiple variables and suggests ways that they can be merged with downscaling techniques in a probabilistic climate change scenario framework to assess the uncertainties associated with future projections. Within hydrological impact studies there is still little consideration given to applied research; how the results can be best used to enable stakeholders and managers to make informed, robust decisions on adaptation and mitigation strategies in the face of many uncertainties about the future. It is suggested that there is a need for a move away from comparison studies into the provision of decision-making tools for planning and management that are robust to future uncertainties; with examination and understanding of uncertainties within the modelling system. Copyright © 2007 Royal Meteorological Society

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Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel ClimateModel (PCM), and the implications of the comparison for a future(2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly(at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at 1/2-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.

http://www.climateknowledge.org/heat_waves/Heat_model_proposal/Wood_Downscaling_Hydrological_Intercomparison_ClimaticChange_2004.pdf

Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change

As the world's largest distributed store of fresh water, ground water plays a central part in sustaining ecosystems and enabling human adaptation to climate variability and change. The strategic importance of ground water for global water and food security will probably intensify under climate change as more frequent and intense climate extremes (droughts and floods) increase variability in precipitation, soil moisture and surface water. Here we critically review recent research assessing the impacts of climate on ground water through natural and human-induced processes as well as through groundwater-driven feedbacks on the climate system. Furthermore, we examine the possible opportunities and challenges of using and sustaining groundwater resources in climate adaptation strategies, and highlight the lack of groundwater observations, which, at present, limits our understanding of the dynamic relationship between ground water and climate.

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Ground water and climate change

Many climate impact applications require the equivalent of point climate observations and are highly sensitive to fine-scale climate variations that are parameterised in coarse-scale models. This is especially true for regions of complex topography, coastal or island locations, and in regions of highly heterogeneous land-cover. This guidance documents reviews statistical methods of estimating point climate from coarse scale climate projections.

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Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods

Summary. A non-homogeneous hidden Markov model is proposed for relating precipitation occurrences at multiple rain-gauge stations to broad scale atmospheric circulation patterns (the socalled 'downscaling problem'). We model a 15-year sequence of winter data from 30 rain stations in south-western Australia. The first 10 years of data are used for model development and the remaining 5 years are used for model evaluation. The fitted model accurately reproduces the observed rainfall statistics in the reserved data despite a shift in atmospheric circulation (and, consequently, rainfall) between the two periods. The fitted model also provides some useful insights into the processes driving rainfall in this region.

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A non‐homogeneous hidden Markov model for precipitation occurrence

[1] This study extends the single parameter precipitation elasticity of streamflow index into a two parameter climate elasticity index, as a function of both precipitation and temperature, in order to assess climatic effects on annual streamflow. Application of the proposed index to two basins indicates that the single parameter precipitation elasticity index may give a general relationship between precipitation and streamflow in some cases, but that it cannot reflect the complicated non-linear relationship among streamflow, precipitation and temperature. For example, for the Spokane River basin the climate elasticity of streamflow index varies from 2.4 to 0.2, for a precipitation increase of 20%, as temperature varies from 1°C lower to 1.8°C higher than the long term mean. Thus a 20% precipitation increase may result in a streamflow increase of 48% if the temperature is 1°C lower but only a 4% increase if the temperature is 1.8°C higher than the long-term mean. The proposed method can be applied to other basins to assess potential climate change effects on annual streamflow. The results of the two case studies can inform planning of long-term basin water management strategies taking into account global change scenarios.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2007WR005890

A two-parameter climate elasticity of streamflow index to assess climate change effects on annual streamflow

Statistical downscaling of general circulation models (GCMs) and limited area models (LAMs) has been promoted as a method for simulating regional- to point-scale precipitation under changed climate conditions. However, several studies have shown that downscaled precipitation is either insensitive to changes in climatic forcing, or inconsistent with the broad-scale changes indicated by the host GCM(s). This has been recently attributed to the omission of the effect that changes in atmospheric moisture content have on precipitation. We describe validation of a nonhomogeneous hid- den Markov model (NHMM) for changed climate conditions and apply it to a network of 30 daily pre- cipitation stations in southwestern Australia. NHMMs fitted to 1 〈 CO2 LAM data were validated by assessing their performance in predicting 2 〈 CO2 LAM precipitation. The inclusion of 850 hPa dew point temperature depression, a predictor reflecting relative (rather than absolute) atmospheric mois- ture content, was found to be crucial to successful performance of the NHMM under 2 〈 CO2 condi- tions. The NHMM validated for the LAM data was fitted to the historical 30 station network and then used to downscale the 2 〈 CO2 LAM atmospheric data, producing plausible predictions of station pre- cipitation under 2 〈 CO2 conditions. Our results highlight that the validation of a statistical downscal- ing technique for present day conditions does not necessarily imply legitimacy for changed climate conditions. Thus statistical downscaling studies that have not attempted to determine the plausibility of their predictions for the changed climate conditions should be viewed with caution.

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Validation of downscaling models for changed climate conditions: case study of southwestern Australia

A stochastic model that relates synoptic atmospheric data to daily precipitation at a network of gages is presented. The model extends the nonhomogeneous hidden Markov model (NHMM) of Hughes et al. by incorporating precipitation amounts. The NHMM assumes that multisite, daily precipitation occurrence patterns are driven by a finite number of unobserved weather states that evolve temporally according to a first-order Markov chain. The state transition probabilities are a function of observed or modeled synoptic scale atmospheric variables such as mean sea level pressure. For each weather state we evaluate the joint distribution of daily precipitation amounts at n sites through the specification of n conditional distributions. The conditional distributions consist of regressions of transformed amounts at a given site on precipitation occurrence at neighboring sites within a set radius. Results for a network of 30 daily precipitation gages and historical atmospheric circulation data in southwestern Australia indicate that the extended NHMM accurately simulates the wet-day probabilities, survival curves for dry- and wet-spell lengths, daily precipitation amount distributions at each site, and intersite correlations for daily precipitation amounts over the 15 year period from 1978 to 1992.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/1999JD900119

Stephen P. Charles

Papers

Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods

A non‐homogeneous hidden Markov model for precipitation occurrence

A two-parameter climate elasticity of streamflow index to assess climate change effects on annual streamflow

Validation of downscaling models for changed climate conditions: case study of southwestern Australia

A spatiotemporal model for downscaling precipitation occurrence and amounts