Catchment-scale assessment of future changes in flood regimes in Norway using stochastic weather generation based on GCM output

<p>Assessment of climate change impacts on hydrological processes is often based on simulations driven by precipitation and temperature series derived from bias-adjusted output from Regional Climate Models (RCMs) using boundary conditions from Global Climate Models (GCMs).  This procedure gives, in principle, locally ‘correct’ results, but is also very demanding of time and resources. In some cases, the dynamical downscaling (i.e. RCM) followed by bias adjustment procedures fails to preserve the climate change signal found in the underlying GCM simulations, thus undermining the reliability of the resulting hydrological simulations. As an alternative, we have used the stochastic weather generator D2Gen (Mezghani and Hingray, 2009, J. Hydrol., 377(3–4): 245–60) to create multiple realisations of catchment-scale precipitation and temperature data series directly from two GCMs (MPI-ESM-LR and NorESM-M1) for the period 1951-2100. D2Gen builds on a suite of Generalised Linear Models (GLMs) to generate precipitation and temperature (i.e. predictands) as a function of explanatory climate variables (or predictors) derived from the GCM such as surface temperature, sea level pressure, westerly and zonal wind components, relative humidity and total precipitation. In this study, we have applied D2Gen on area-averaged precipitation and temperature data for 18 hydrological catchments distributed across Norway. Weather generation is then undertaken based on the expected mean modelled by the GLM plus a noise component to account for local features and random effects introduced by local physical processes that are otherwise not accounted for.  The weather generator was trained for each catchment based on observed precipitation and temperature series for the period 1985-2014, and stochastic weather generation was then performed to construct catchment-scale precipitation and temperature series for the period 1951-2100 that were further used in hydrological simulations based on the HBV hydrological model for the 18 catchments. </p><p>Validation of the D2Gen results was based on comparisons with observed annual, seasonal and maximum temperature and precipitation, as well as with observed average annual and maximum annual discharge using 30-year time slices.  Comparisons were also made with projected changes generated from hydrological simulations based on a) EURO-CORDEX RCM simulations (MPI-ESM-LR_SMHI-RCA4 and MPI_CCLM-CM5) for the MPI GCM; and b) high resolution (4 km) simulations with the WRF model driven by a bias-corrected NorESM GCM.  Results suggest that in most catchments the D2gen approach performs equally well or sometimes even better than the traditional ‘bias-corrected RCM approach’ in reproducing the 30-year average annual flood during the historical period. We also found that for the projection period, the simulations based directly on the GCM output (via d2gen) tend to give slightly larger projected increases in the average annual flood in rainfall-dominated catchments than does the use of bias-corrected RCM simulations. Overall, the results indicate that the D2Gen weather generator offers a feasible alternative approach for projecting catchment-scale impacts on changes in flood regimes under a changing climate.  It also offers the significant advantage that it can be used directly with the CMIP-6 ensemble of GCMs without the time delay associated with the production of the next round of EURO-CORDEX based simulations.</p>

Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization

<div><div><div><div>The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast of the losses. New soiling rate extraction methods are proposed to reflect the seasonal variability of the soiling rates and, for this reason, are found to identify the most convenient cleaning day with the highest accuracy for the investigated sites. Also, we present an approach that could be used to predict future soiling losses through the implementation of stochastic weather generation algorithms whose ability to identify in advance the best cleaning schedule is also successfully tested. The methods presented in this article can optimize the operation and maintenance schedule and could make it possible, in the future, to predict soiling losses through analysis based only on environmental parameters, such as rainfall and particulate matter, without the need of long-term soiling data.</div></div></div></div>

Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization

<div><div><div><div>The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast of the losses. New soiling rate extraction methods are proposed to reflect the seasonal variability of the soiling rates and, for this reason, are found to identify the most convenient cleaning day with the highest accuracy for the investigated sites. Also, we present an approach that could be used to predict future soiling losses through the implementation of stochastic weather generation algorithms whose ability to identify in advance the best cleaning schedule is also successfully tested. The methods presented in this article can optimize the operation and maintenance schedule and could make it possible, in the future, to predict soiling losses through analysis based only on environmental parameters, such as rainfall and particulate matter, without the need of long-term soiling data.</div></div></div></div>

Observed Spatiotemporal Trends in Intense Precipitation Events across United States: Applications for Stochastic Weather Generation

An increasing focus of climate change studies is the projection of storm events characterized by heavy, very heavy, extreme, and/or intense precipitation. Projected changes in the spatiotemporal distributions of such intense precipitation events remain uncertain due to large measures of variability in both the definition and evidence of increased intensity in the upper percentile range of observed daily precipitation distributions, particularly on a regional basis. As a result, projecting changes in future precipitation at the upper tail of the distribution (i.e., the heavy to heaviest events), such as through the use of stochastic weather generator programs, remains challenging. One approach to address this challenge is to better define what constitutes intense precipitation events and the degree of location-specific adjustment needed for the weather generator programs to appropriately account for potential increases in precipitation intensity due to climate change. In this study, we synthesized information on categories of intense precipitation events and assessed reported trends in the categories at national and regional scales within the context of applying this information to stochastic weather generation. Investigations of adjusting weather generation models to include long-term regional trends in intense precipitation events are limited, and modeling trends in site-specific future precipitation distributions forecasted by weather generator programs remains challenging. Probability exceedance curves and variations between simulated and observed distributions can help in modeling and assessment of trends in future extreme precipitation events that reflect changes in precipitation intensity due to climate change.

A Global Assessment of the Spatial Distribution of Precipitation Occurrence

The spatial distribution of precipitation occurrence has important implications for numerous applications ranging from defining cloud radiative effects to modeling hydrologic runoff, statistical downscaling, and stochastic weather generation. This paper introduces a new method of describing the spatial characteristics of rainfall and snowfall that takes advantage of the high sensitivity and high resolution of the W-band cloud precipitation radar aboard CloudSat. The resolution dependence of precipitation occurrence is described by a two-parameter exponential function defined by a shape factor that governs the variation in the distances between precipitation events and a scale length that represents the overall probability of precipitation and number density of distinct events.Geographic variations in the shape factor and scale length are consistent with large-scale circulation patterns and correlate with environmental conditions on local scales. For example, a large contrast in scale lengths between land and ocean areas reflects the more extensive, widespread nature of precipitation over land than over ocean. An analysis of warm rain in the southeast Pacific reveals a shift from frequent isolated systems to less frequent but more regularly spaced systems along a transect connecting stratocumulus and trade cumulus cloud regimes. A similar analysis during the Amazon wet season reveals a relationship between the size and frequency of convection and zonal wind direction with precipitation exhibiting a more oceanic character during periods of westerly winds. These select examples demonstrate the utility of this approach for capturing the sensitivity of the spatial characteristics of precipitation to environmental influences on both local and larger scales.