Temporal Downscaling Research Articles

A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data

Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the “hysteresis phenomenon” of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets.

A downscaling-merging method for high-resolution daily precipitation estimation

Due to the strong variation of precipitation in time and space, it is necessary to generate accurate precipitation data with high spatiotemporal resolution. This is essential for many applications such as hydrological and meteorological analysis. However, achieving such data over a large area remains a tremendous challenge. In this study, we presented a downscaling-merging method to solve this problem. First, an integrated “temporal upscaling – spatial downscaling – temporal downscaling” strategy is introduced to spatially downscale satellite-based daily precipitation data to a high spatial resolution of 0.01°. The downscaled daily precipitation estimates are then merged with gauge observations in a multivariate geostatistical framework. To avoid the adverse effects of sparse gauges, the semivariogram model of daily precipitation is inferred from the downscaled results. The method was applied to the Tropical Rainfall Measuring Mission (TRMM) daily precipitation product covering central China’s Henan Province for the period 1 January 2015–31 December 2016. The experimental results show that: (1) the downscaling strategy can effectively spatially downscale the TRMM daily precipitation product, maintaining not only the accuracy of the original TRMM data, but also providing greater spatial detail; (2) a seasonal timescale is appropriate for downscaling analysis; and (3) the merging scheme greatly enhances the accuracy of downscaled daily precipitation estimates, reducing the mean absolute error and root mean square error by 57% and 42%, respectively, and increasing the critical success index by 0.17. The proposed method provided an efficient solution for producing accurate daily precipitation data with high spatial resolution over a large area.

Temperature-Driven Rise in Extreme Sub-Hourly Rainfall

AbstractEstimates of present and future extreme sub-hourly rainfall are derived from a daily spatial followed by a sub-daily temporal downscaling, the latter of which incorporates a novel, and crucial, temperature sensitivity. Specifically, daily global climate fields are spatially downscaled to local temperature T and precipitation P, which are then disaggregated to a temporal resolution of 10 min using a multiplicative random cascade model. The scheme is calibrated and validated with a group of 21 station records of 10-min resolution in Germany. The cascade model is used in the classical (denoted as MC) and in the new T-sensitive (MC+) version, which respects local Clausius–Clapeyron (CC) effects such as CC scaling. Extreme P is positively biased in both MC versions. Observed T sensitivity is absent in MC but well reproduced by MC+. Long-term positive trends in extreme sub-hourly P are generally more pronounced and more significant in MC+ than in MC. In units of 10-min rainfall, observed centennial trends in annual exceedance counts (EC) of P > 5 mm are +29% and in 3-yr return levels (RL) +27%. For the RCP4.5-simulated future, higher extremes are projected in both versions MC and MC+: per century, EC increases by 30% for MC and by 83% for MC+; the RL rises by 14% for MC and by 33% for MC+. Because the projected daily P trends are negligible, the sub-daily signal is mainly driven by local temperature.

Hydrological modelling at multiple sub-daily time steps: Model improvement via flux-matching

Consistent hydrological models for multiple time steps are needed to respond to the operational requirement of using a model for different objectives and with varied data sets. In this paper, we propose a rationale for multi-time step model development based on the temporal consistency of the fluxes modelled. The new methodology is applied for the temporal downscaling of a lumped rainfall-runoff model (GR4J) from daily to sub-hourly time steps, within a coherent modelling framework. The modelling tests are performed at eight time steps from daily to 6-min, for a set of 240 French catchments, for which 2400 flood events were selected. To identify an improved multi-time step model structure, a two-step approach is followed. First, we propose a model diagnosis to evaluate the internal behaviour of the baseline model across time steps, whereby a significant inconsistency was found in the simulated water fluxes, especially interception. Second, we recommend a prognosis to improve the scale invariance of the modelled water balance by matching the model fluxes across time scales. The new model structure retained for sub-daily time steps is derived from the daily baseline model by refining the representation of the interception process, so that the flux-matching condition is satisfied. A complementary modification of the groundwater exchange function is also resumed following a previous study. The structural changes are motivated by the improvement of model flux consistency across time steps, while also improving model performance. This new dual paradigm for model identification, based on both flux coherence and output accuracy, also results in more robust model parameters across time steps.

Machine Learning for Projecting Extreme Precipitation Intensity for Short Durations in a Changing Climate

Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against extreme precipitation. It is important to incorporate the potential threat from climate change into the computation of IDF curves. Most existing works that have achieved this goal were based on Generalized Extreme Value (GEV) analysis combined with various circulation model simulations. Inspired by recent works that used machine learning algorithms for spatial downscaling, this paper proposes an alternative method to perform projections of precipitation intensity over short durations using machine learning. The method is based on temporal downscaling, a downscaling procedure performed over the time scale instead of the spatial scale. The method is trained and validated using data from around two thousand stations in the US. Future projection of IDF curves is calculated and discussed.

Development of an ANN based corrective algorithm of the operational ECMWF global horizontal irradiation forecasts

This paper proposes a corrective algorithm for improving the accuracy of global horizontal irradiation (GHI) forecasts obtained from the numerical weather prediction (NWP) model of the European Centre for Medium-range Weather Forecast (ECMWF). Firstly, GHI forecasts were compared with experimental values from two ground-based stations located at the south of Portugal (Évora and Sines), and the influence of Sun-Earth geometry and atmospheric variables on the differences between predictions and measurements was analysed in order to identify the most relevant parameters. These differences are shown to be correlated mainly with the clearness index, solar zenith angle, mean air temperature, relative air humidity and total water column. Since the ECMWF model directly or indirectly provides all these variables, it is possible to estimate the bias of the predicted GHI as a function of forecast data taking as reference the measurements, which means that an algorithm based on correlations of such parameters can be used to correct forecasts in an operational time horizon. With that goal, an artificial neural network (ANN) based algorithm was developed in this work to improve GHI predictions, including also as input the global solar irradiation predicted by a reference clear sky model. The internal structure of the ANN was optimised, and a spatial and temporal downscaling procedure was also developed to obtain half-hour irradiation values. This algorithm was tested against the original ECMWF forecasts and a persistence model for four locations with different orography and climate as well as for various sky conditions (overcast, partly cloudy and clear sky), showing that it successfully improves the model predictions. Higher values of a Global Performance Index (GPI) based on seven statistical indicators and Forecast Score (FS) were found for the algorithm simulations, e.g. respectively 1.066 and 0.348 when considering all the locations and cloud cover conditions, while for the original ECMWF predictions the GPI is −1.874 and the FS is 0.282. This algorithm is useful if integrated into energy management tools of solar energy systems, namely low/medium temperature solar thermal and photovoltaic systems.

An Improved Spatial–Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China’s Qilian Mountains

Remote sensing techniques provide data on the spatial–temporal distribution of environmental parameters over regions with sparse ground observations. However, the resolution of satellite precipitation data is too coarse to be applied to hydrological and meteorological research at basin scales. Downscaling research using coarse remote sensing data to obtain high-resolution precipitation data is significant for the development of basin-scale research. Here, we propose improvements to a spatial–temporal method for downscaling satellite precipitation. The improved method uses a nonlinear regression model and introduces longitude and latitude based on processed normalized difference vegetation index (NDVI) and a digital elevation model (DEM) to stimulate precipitation in the Qilian Mountains during 2006–2015. The final downscaled annual precipitation (FDAP) results are corrected by observed data to obtain corrected final downscaled annual precipitation (CFDAP) datasets. For temporal downscaling, monthly downscaled data are the corrected monthly ratio multiplied by the corresponding downscaled annual datasets. The results indicated that processed NDVI (PNDVI) reflected spatial precipitation patterns more accurately than the original NDVI. The accuracy was significantly improved when the final downscaled annual precipitation data were corrected by observed data. The average annual root mean square error (RMSE) from 2006 to 2015 of CFDAP was 66.48 and 83.07 mm less than that of FDAP and original Tropical Rainfall Measuring Mission (TRMM) data, respectively. Compared with previous methods, which use NDVI and/or DEM to downscale TRMM, the accuracy of FDAP and CFDAP from the improved method was higher, and the RMSE decreased on average by 13.63 and 80.11 mm. The RMSE of monthly data from corrected monthly ratio (CMR) decreased on average by 4.93 mm over monthly data from previous monthly ratio (PMR). In addition, the accuracy of the original satellite data affected the initial downscaling results but had no significant effects on the corrected downscaling results.

Temporal Downscaling of IDF Curves Applied to Future Performance of Local Stormwater Measures

Low-impact development (LID) structures are combined with traditional measures to manage stormwater and cope with increased runoff rates originating from heavy urbanization and climate change. As the use of LIDs for climate adaptation increases, practitioners need more knowledge on LID performance in future climates for successful planning and implementation. In this study, temporal downscaling of regional climate projections for three cities in Norway is performed, using the concept of scale invariance to downscale the distribution of extreme precipitation from daily to sub-daily timescales. From this, local-scale intensity-duration-frequency (IDF) curves for future precipitation were obtained. Using climate projections of daily temporal resolution as input to water balance models and the obtained IDF relationships as input to event-based models allowed for assessing the retention capacity, peak flow reduction potential and pollution control of three different types of LIDs: green roofs, bioretention cells, and detention basins. The downscaling resulted in large local variations in presumed increase of both precipitation amount and intensity, contradicting current design recommendations in Norway. Countrywide, a decrease in the overall LID performance was found, although some positive effects of temperature rises were detected. The study illustrated the importance of evapotranspiration- and infiltration-based processes in future stormwater management and how coupling of LID structures in series can significantly reduce required detention volumes.

Effect of RCM Temporal Resolution on Estimating Future IDF Curves

Recent domestic researches on the future design rainfall have been conducted by applying various temporal downscaling techniques based on daily temporal resolution data produced from Regional Climate Model (RCM) provided by Korea Meterological Administration (KMA) and CORDEX. However, no matter what temporal downscaling techniques are applied, many assumptions are needed in the process of estimating the design rainfall for sub-daily duration from daily rainfall time series. In this study, we investigated the effect of RCM temporal resolution on the estimation of future IDF curves. For this purpose, temporal resolution 3-hour and 24-hour rainfall data were constructed using observed hourly rainfall time series of KMA of Busan station. And, the derived IDF curves were compared with the IDF curve derived from temporal resolution 1-hour rainfall time series, respectively. As a result, it was confirmed that the IDF curve derived from the temporal resolution 3-hour rainfall data reproduces the IDF curve derived from the observed hourly rainfall time series better. Also, future IDF curves were derived from temporal resolution 3-hour future rainfall time series that dynamically downscaled at Busan station using RCM (WRF), general climate model HadGEM2-AO driven by RCP 8.5 scenario. Finally, the effect of RCM's temporal resolution on the estimation of future IDF curves was analyzed by comparing with the future IDF curve derived from temporal resolution 3-hour and 24-hour future rainfall time series. Keywords: Climate Change, IDF Curve, RCM, Temporal Resolution

A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property

To improve our capacity to use available wind speed data, it is necessary to develop a new statistical temporal downscaling method that uses one or a few input variables of any temporal scale for mean wind speed data to obtain wind statistics at finer temporal resolution. In the present study, a novel statistical temporal downscaling method for wind speed statistics and probability distribution is proposed. The proposed method uses the temporal structure to downscale the wind speed statistics to a fine temporal scale without the use of additional variables. The Weibull distribution of the hourly and 10-min mean wind speed data is obtained by the downscaled wind speed statistics. The proposed method provides the downscaled Weibull distribution of fine temporal wind speed data using coarse temporal wind statistics. Particularly, the use of sub-daily mean wind speed data in the downscaling of the wind speed Weibull distribution leads to good estimation precision. The Weibull distribution downscaled by the proposed method successfully reproduces the wind power density based on the wind potential energy estimation.

Reaching the human scale: A spatial and temporal downscaling approach to the archaeological implications of paleoclimate data

Assessing the implications of paleoclimatic and paleoenvironmental data at temporal and spatial scales that would have directly intersected with human decision-making and activity is a fundamental archaeological challenge. This paper addresses this challenge by presenting a spatial and temporal downscaling method that can provide quantitative high-spatio-temporal-resolution estimates of the local consequences of climatic change. Using a case study in Provence (France) we demonstrate that a centennial-scale Mediterranean-wide model of Holocene climate, in conjunction with modern geospatial and climate data, can be used to generate explicit and solidly-grounded monthly estimates of temperature, precipitation, and cloudiness at landscape scales and with annual resolution, enabling consideration of climate variability at human scales and meeting the data requirements of socioecological models focused on human activity. While the results are not reconstructions – that is, particular values are single realizations, consistent with the coarse-grained data but not individually empirically derived nor unique solutions – they provide a more suitable basis for assessing the human consequences of climate change than can coarse-grained data.

Tethys – A Python Package for Spatial and Temporal Downscaling of Global Water Withdrawals

Downscaling of water withdrawals from regional/national to local scale is a fundamental step and also a common problem when integrating large scale economic and integrated assessment models with high-resolution detailed sectoral models. Tethys, an open-access software written in Python, is developed with statistical downscaling algorithms, to spatially and temporally downscale water withdrawal data to a finer scale. The spatial resolution will be downscaled from region/basin scale to grid (0.5 geographic degree) scale and the temporal resolution will be downscaled from year to month. Tethys is used to produce monthly global gridded water withdrawal products based on estimates from the Global Change Assessment Model (GCAM). Funding statement: PNNL is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830

Temporal downscaling of test reference years: Effects on the long-term evaluation of photovoltaic systems

Abstract Representative meteorological data from a given location are necessary to assess the long-term performance of photovoltaic (PV) systems. Test reference years (TRYs) or typical meteorological years (TMYs) are widely used as input to PV models. Most of current procedures propose the construction of TRYs by concatenating 12 months belonging to different years of a dataset. This paper evaluates the effects of the temporal downscaling of typical periods that compose different TRYs on the long-term assessment of PV systems. The Festa-Ratto TRY, WYSS, EN ISO 15927-4 TRY, TMY3, TGY and TDY are used. Thus, an adapted version of these six methodologies aimed at the selection of typical days rather than months is proposed. The electricity production obtained by simulation for daily and monthly TRYs is compared with simulations performed for each actual year of the dataset. This analysis is performed for seven locations in the USA considering a 5.6 kWp grid-connected PV system. The results reveal that the timescale reduction improves the behavior of Festa-Ratto TRY, WYSS, TMY3, TDY and TDY when estimating the long-term production of a PV system considering the hourly, daily, monthly and annual timescales, while the modified EN ISO 15927-4 TRY performs worse than its monthly version.

Assessing the robustness of raingardens under climate change using SDSM and temporal downscaling.

Climate change is expected to lead to higher precipitation amounts and intensities causing an increase of the risk for flooding and combined sewer overflows in urban areas. To cope with these changes, water managers are requesting practical tools that can facilitate adaptive planning. This study was carried out to investigate how recent developments in downscaling techniques can be used to assess the effects of adaptive measures. A combined spatial-temporal downscaling methodology using the Statistical DownScaling Model-Decision Centric (SDSM-DC) and the Generalized Extreme Value distribution was applied to project future precipitation in the city of Bergen, Norway. A raingarden was considered a potential adaptive measure, and its performance was assessed using the RECARGA simulation tool. The benefits and limitations of using the proposed method have been demonstrated and compared to current design practices in Norway. Large differences in the raingarden's performance with respect to percentage overflow and lag-time reduction were found for varying projections. This highlights the need for working with a range of possible futures. Further, it was found that Ksat was the determining factor for peak-flow reduction and that different values of Ksat had different benefits. Engineering flexible solutions by combining measures holding different characteristics will induce robust adaptation.

The impact of climate change on extreme precipitation in Sicily, Italy

AbstractIncreasing precipitation extremes are one of the possible consequences of a warmer climate. These may exceed the capacity of urban drainage systems, and thus impact the urban environment. Because short‐duration precipitation events are primarily responsible for flooding in urban systems, it is important to assess the response of extreme precipitation at hourly (or sub‐hourly) scales to a warming climate. This study aims to evaluate the projected changes in extreme rainfall events across the region of Sicily (Italy) and, for two urban areas, to assess possible changes in Depth‐Duration‐Frequency (DDF) curves. We used Regional Climate Model outputs from Coordinated Regional Climate Downscaling Experiment for Europe area ensemble simulations at a ~12 km spatial resolution, for the current period and 2 future horizons under the Representative Concentration Pathways 8.5 scenario. Extreme events at the daily scale were first investigated by comparing the quantiles estimated from rain gauge observations and Regional Climate Model outputs. Second, we implemented a temporal downscaling approach to estimate rainfall for sub‐daily durations from the modelled daily precipitation, and, lastly, we analysed future projections at daily and sub‐daily scales. A frequency distribution was fitted to annual maxima time series for the sub‐daily durations to derive the DDF curves for 2 future time horizons and the 2 urban areas. The overall results showed a raising of the growth curves for the future horizons, indicating an increase in the intensity of extreme precipitation, especially for the shortest durations. The DDF curves highlight a general increase of extreme quantiles for the 2 urban areas, thus underlining the risk of failure of the existing urban drainage systems under more severe events.

Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China

Abstract. Projected hydrological variability is important for future resource and hazard management of water supplies because changes in hydrological variability can cause more disasters than changes in the mean state. However, climate change scenarios downscaled from Earth System Models (ESMs) at single sites cannot meet the requirements of distributed hydrologic models for simulating hydrological variability. This study developed multisite multivariate climate change scenarios via three steps: (i) spatial downscaling of ESMs using a transfer function method, (ii) temporal downscaling of ESMs using a single-site weather generator, and (iii) reconstruction of spatiotemporal correlations using a distribution-free shuffle procedure. Multisite precipitation and temperature change scenarios for 2011–2040 were generated from five ESMs under four representative concentration pathways to project changes in streamflow variability using the Soil and Water Assessment Tool (SWAT) for the Jing River, China. The correlation reconstruction method performed realistically for intersite and intervariable correlation reproduction and hydrological modeling. The SWAT model was found to be well calibrated with monthly streamflow with a model efficiency coefficient of 0.78. It was projected that the annual mean precipitation would not change, while the mean maximum and minimum temperatures would increase significantly by 1.6 ± 0.3 and 1.3 ± 0.2 °C; the variance ratios of 2011–2040 to 1961–2005 were 1.15 ± 0.13 for precipitation, 1.15 ± 0.14 for mean maximum temperature, and 1.04 ± 0.10 for mean minimum temperature. A warmer climate was predicted for the flood season, while the dry season was projected to become wetter and warmer; the findings indicated that the intra-annual and interannual variations in the future climate would be greater than in the current climate. The total annual streamflow was found to change insignificantly but its variance ratios of 2011–2040 to 1961–2005 increased by 1.25 ± 0.55. Streamflow variability was predicted to become greater over most months on the seasonal scale because of the increased monthly maximum streamflow and decreased monthly minimum streamflow. The increase in streamflow variability was attributed mainly to larger positive contributions from increased precipitation variances rather than negative contributions from increased mean temperatures.

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

Abstract. While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.

Multisite and multivariate GCM downscaling using a distribution-free shuffle procedure for correlation reconstruction

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 72:141-151 (2017) - DOI: https://doi.org/10.3354/cr01460 Multisite and multivariate GCM downscaling using a distribution-free shuffle procedure for correlation reconstruction Zhi Li*, Xiaoping Shi, Jingjing Li State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, PR China *Corresponding author: lizhibox@126.com ABSTRACT: Reconstructing the multisite and multivariate correlations of meteorological variables during downscaling of general circulation model (GCM) outputs is extremely important for hydrological variability simulation. To this end, this study proposes a 2-step approach to downscale GCMs considering the correlations between sites and variables. The first step generates single-site climate change scenarios for daily precipitation and maximum and minimum temperature. This step is open to any downscaling technique, since the subsequent correlation reconstruction does not change the statistical parameters. This study employed a quantile mapping method for spatial downscaling and the Richardson-type weather generator for temporal downscaling. The second step re-correlates the generated daily and site-based climate change scenarios by a distribution-free shuffling procedure. We used precipitation and maximum and minimum temperature data from a network of 15 weather stations and 1 GCM to develop and evaluate the method. Results showed that the proposed method satisfactorily preserved the downscaled statistical parameters and intersite and intervariable correlations. By simultaneously shuffling the data for all stations and all 3 variables for each month, the proposed approach is easy to implement, as well as being computation-saving, and provides a rational option for multisite and multivariate GCM downscaling. KEY WORDS: GCM downscaling · Spatial structure · Temporal pattern · Weather generator Full text in pdf format PreviousNextCite this article as: Li Z, Shi X, Li J (2017) Multisite and multivariate GCM downscaling using a distribution-free shuffle procedure for correlation reconstruction. Clim Res 72:141-151. https://doi.org/10.3354/cr01460 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 72, No. 2. Online publication date: May 18, 2017 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2017 Inter-Research.

Downscaling wind and wavefields for 21st century coastal flood hazard projections in a region of complex terrain

AbstractWhile global climate models (GCMs) provide useful projections of near‐surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically downscaled GCM projections from Multivariate Adaptive Constructed Analogues provide daily averaged near‐surface winds at an appropriate spatial resolution for wave modeling within the orographically complex region of San Francisco Bay, but greater resolution in time is needed to capture the peak of storm events. Short‐duration high wind speeds, on the order of hours, are usually excluded in statistically downscaled climate models and are of key importance in wave and subsequent coastal flood modeling. Here we present a temporal downscaling approach, similar to constructed analogues, for near‐surface winds suitable for use in local wave models and evaluate changes in wind and wave conditions for the 21st century. Reconstructed hindcast winds (1975–2004) recreate important extreme wind values within San Francisco Bay. A computationally efficient method for simulating wave heights over long time periods was used to screen for extreme events. Wave hindcasts show resultant maximum wave heights of 2.2 m possible within the Bay. Changes in extreme over‐water wind speeds suggest contrasting trends within the different regions of San Francisco Bay, but 21th century projections show little change in the overall magnitude of extreme winds and locally generated waves

A Novel Spatiotemporal Statistical Downscaling Method for Hourly Rainfall

Finer spatiotemporal resolution rainfall data is essential for assessing hydrological impacts of climate change on medium and small basins. However, existing methods pay less attention to the inter-day correlation and diurnal cycle, which can strongly influence the hydrological cycle. To address this problem, we present a spatiotemporal downscaling method that is capable of reproducing the inter-day correlation, the diurnal cycle, and rainfall statistics on daily and hourly scales. The large-scale datasets, which we obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis dataset (NNR) and general circulation model (GCM) outputs, and local rainfall data are analyzed to assess the impacts of climate change on rainfall. Our proposed method consists of two steps: spatial downscaling and temporal downscaling. We apply spatial downscaling first to obtain the relationship between large-scale datasets and daily rainfall at a site scale using a k-nearest neighbor method (KNN). Then, we conduct an hourly downscaling of daily rainfall in the second step using a genetic algorithm-based KNN (GAKNN) with the inter-day correlation and the diurnal cycle. Furthermore, we analyzed changes in rainfall statistics for the periods 2046–2065 and 2081–2100 under the A2, A1B, and B1 scenarios of the third generation Coupled Global Climate Model (CGCM3.1) and Bergen Climate Model version 2 (BCM2.0). An application of our proposed method to the Shihmen Reservoir basin (Taiwan) has shown that it could accurately reproduce local rainfall and its statistics on daily and hourly scales. Overall, the results demonstrated that the proposed spatiotemporal method is a powerful tool for downscaling hourly rainfall data from a large-scale dataset. The understanding of future changes of rainfall characteristics through our proposed method is also expected to assist the planning and management of water resources systems.