Multi-scale Temporal Variability Research Articles

Multiscale variability of China’s historical flood/drought index and precipitation teleconnections with ENSO using wavelet analyses

Studies of the low-frequency variabilities of key climate variables are often handicapped by the limited length of available instrumental observations. To tackle this, the use of a set of historical flood/drought index (FDI) spanning from AD 1470 to 2000 for 120 sites in China has been made to investigate the multiscale temporal variability of annual precipitation by applying wavelet methods. The analyses reveal oscillating components of the FDI time series from the decadal to multi-decadal, and to the quasi-centennial range, as well as in the interannual range. Furthermore, the relationships of the FDI with the dominant mode of oscillations in the coupled ocean–atmosphere system, i.e., the El Niño-Southern Oscillation (ENSO) index, on a range of time scales have been probed by cross wavelet transform and wavelet coherence methods. Statistically significant coherence between FDI and ENSO index time series has been found for regions in eastern China south of the Yangtze River (inclusive) at the decadal to multi-decadal time scale (10- to 50-yr) after 1750, as well as for north China on the 10- to 30-year range in the eighteenth century. The FDI is less coherent with the ENSO index for other regions of China. The results of the present study may add to our understanding of the connections between long-term changes of annual precipitation and large-scale oscillations in the coupled ocean–atmosphere system, and provide a scientific basis for developing policies to adapt to future changes in water abundance.

The Importance of the Orbital Parameters for the Indian Summer Monsoon During the Mid-Holocene, as Deciphered From Atmospheric Model Experiments

Proxy and model-based studies suggest multi-scale temporal variability in the Indian summer monsoon (ISM). In this study, using the CESM1 atmospheric general circulation model, we carried out multiple ensemble AGCM simulations for the Mid-Holocene (MH; ≈ 6 kyr BP), Medieval Warm Period (MWP; ≈ 1 kyr BP), Little Ice Age (LIA; ≈ 0.35 kyr BP), and Historical (HS; ≈ CE 2000) periods. We used the PMIP3/CMIP5 boundary conditions for this purpose. Our simulations indicate that the ISM during the MH was stronger compared to HS and the rainfall higher, in agreement with several proxy studies. The experiments also suggest that the ISM rainfall (ISMR) was higher during MWP relative to the LIA in agreement with our earlier results from the PMIP3 models. A relatively northward migration of the ITCZ over the Indian region and strengthening of the neighboring subtropical high over the northwestern Pacific, both associated with stronger insolation associated with the obliquity and precision during the MH, seem to be important reason Indian summer monsoon during the MH.

Multi-scale temporal variability in biological-physical associations in the NE Chukchi Sea

In high-latitude marine ecosystems, traditional net sampling is constrained to the ice-free season, resulting in an incomplete understanding of ecosystem structure and dynamics. Using 4 years of continuous acoustic and environmental measurements from a NE Chukchi Sea subsurface mooring, we assessed fish and zooplankton abundance and behavior relative to environmental factors over a wide range of temporal scales. We applied wavelet analysis to these high-resolution, multi-year, concurrent, and co-located datasets to identify temporal scales of variability in environmental conditions and density and vertical distribution metrics for pelagic fish and zooplankton. Biological variability occurs mainly at distinct diel (24-h), seasonal (3–6-month), and annual (9–12-month) scales. Diel patterns are present throughout the year but are strongest in autumn when day-night cycles are pronounced. Seasonal variability in zooplankton metrics (3–4 months) is mainly associated with sea ice patterns that may also regulate the onset of primary production. Seasonal variations in fish metrics are associated most closely with salinity patterns (~ 3 months) and slower changes in water temperature (~ 6 months). Annual cycles in biological characteristics are influenced by year-round variations in water temperature, sea ice concentration, light irradiance, and wind. Wind and salinity-associated variability in biological metrics was observed at scales of 6–28 days. Scale-dependent biological and environmental associations vary through time and emphasize the importance of high-resolution long-term studies for comprehensive ecosystem characterizations. Our results identify necessary scales of observation in Arctic monitoring programs for improved prediction and detection of biological responses to rapidly changing environments.

Multi-scale temporal variability in meltwater contributions in a tropical glacierized watershed

Abstract. Climate models predict amplified warming at high elevations in low latitudes, making tropical glacierized regions some of the most vulnerable hydrological systems in the world. Observations reveal decreasing streamflow due to retreating glaciers in the Andes, which hold 99 % of all tropical glaciers. However, the timescales over which meltwater contributes to streamflow and the pathways it takes – surface and subsurface – remain uncertain, hindering our ability to predict how shrinking glaciers will impact water resources. Two major contributors to this uncertainty are the sparsity of hydrologic measurements in tropical glacierized watersheds and the complication of hydrograph separation where there is year-round glacier melt. We address these challenges using a multi-method approach that employs repeat hydrochemical mixing model analysis, hydroclimatic time series analysis, and integrated watershed modeling. Each of these approaches interrogates distinct timescale relationships among meltwater, groundwater, and stream discharge. Our results challenge the commonly held conceptual model that glaciers buffer discharge variability. Instead, in a subhumid watershed on Volcán Chimborazo, Ecuador, glacier melt drives nearly all the variability in discharge (Pearson correlation coefficient of 0.89 in simulations), with glaciers contributing a broad range of 20 %–60 % or wider of discharge, mostly (86 %) through surface runoff on hourly timescales, but also through infiltration that increases annual groundwater contributions by nearly 20 %. We further found that rainfall may enhance glacier melt contributions to discharge at timescales that complement glacier melt production, possibly explaining why minimum discharge occurred at the study site during warm but dry El Niño conditions, which typically heighten melt in the Andes. Our findings caution against extrapolations from isolated measurements: stream discharge and glacier melt contributions in tropical glacierized systems can change substantially at hourly to interannual timescales, due to climatic variability and surface to subsurface flow processes.

The Multi-Scale Temporal Variability of Extreme Precipitation in the Source Region of the Yellow River

Changes in extreme precipitation are critical to assess the potential impacts of climate change on human and natural systems. This paper provides a comprehensive investigation on the multi-scale temporal variability of extreme precipitation in the Source Region of the Yellow River (SRYR). The statistical analysis explores multi-scale extreme precipitation variability ranging from short to long term, including seasonal, annual, and inter-annual variations at different locations in the SRYR. The results suggest that seasonal patterns of extreme precipitation do not always follow the seasonal pattern of total precipitation. Heavy precipitation mostly happens during the period from May and October with July as the peak, while dry conditions are mostly seen in winter seasons. However, there are no significant annual trends for most indices at most locations. The extreme heavy precipitation presents an increasing trend at high elevation and decreasing trend at low elevation. The extreme dry condition presents more consistently decreasing trends at nearly all locations. Long-term analyses indicate that most of the selected indices except average daily intensity display multi-year bands ranging from 2 to 8 years which is probably due to the effects of El Niño–Southern Oscillation (ENSO). A further evaluation on how the ENSO events would impact extreme precipitation shows that eastern Pacific warming (EPW) and central Pacific warming (CPW) would bring less extreme heavy precipitation compared to normal years. These results can provide a beneficial reference to understand the temporal variability of extreme precipitation in the SRYR.

Accounting for low solar resource days to size 100% solar microgrids power systems in Africa

Abstract In many regions worldwide, the electrification of rural areas is expected to be partly achieved through micro power grids. Compliance with the COP21 conference requires that such systems mainly build on renewable energy sources. To deliver a high power and quality service may be difficult to be achieved, especially when micro-grids are based on variable renewable sources. We here explore the multiscale temporal variability of the local solar resource in Africa and its implication for the development of 100% solar systems. Using high resolution satellite data of global horizontal irradiance (GHI) for a 21-year period (1995–2015), we characterize the seasonality and temporal variability of the local resource. We focus on its low percentile values which give a first guess on the size of the solar panels surface required for the micro-grid to achieve a given quality service. We assess the characteristics and especially persistence of the low resource situations, for which the local demand would not be satisfied. We finally assess how the ability of electricity consumers for some day-to-day flexibility (e.g. via the postponement of part of one day as demand to the next), would help to achieve the design level of service quality with a smaller microgrid system.

Multiscale temporal variability of flow-sediment relationships during the 1950s–2014 in the Loess Plateau, China

Flow-sediment relationships provide insights into the erosion and transport of materials within catchments. Investigating the flow-sediment relationships across multiple timescales can reveal trends related to the effects of natural and human-caused changes to catchments. This study chose fourteen main catchments in the Loess Plateau of China, which are the major sediment sources of Yellow River, to extract the temporal patterns of flow-sediment relationships during the 1950s–2014 period. The study revealed quantifiable effects of land use/cover changes (LUCC) on the variability of flow-sediment relationships. The LUCC induced by the soil and water conservation measures (SWCMs) during 1970–1999 and vegetation restoration campaign since 2000 resulted in significant reductions of annual streamflow, sediment yield and sediment concentration. The mean daily sediment concentration under different flow conditions also decreased greatly. The flow-sediment relationships revealed time dependence over different stages and time scales. Before 2000, the annual and monthly flow-sediment relationships could be generally characterized by the linear functions, and the power-law sediment rating curve was able to describe the daily flow-sediment relationship. As vegetation restoration campaign took effect, the flow-sediment relationship became much weaker, which could not be described by a clear relationship. This suggests that the variability of flow-sediment relationships over the entire period was mainly regulated by the vegetation restoration campaign. In addition, the sediment concentration exhibited a linear decreasing relationship with the area under land use/cover treatment. In this way, the study has brought out the roles of ecological restoration measures in controlling the temporal variability of flow-sediment relationships at the catchment scale.

Multiscale temporal variability and regional patterns in 555 years of conterminous U.S. streamflow

AbstractThe development of paleoclimate streamflow reconstructions in the conterminous United States (CONUS) has provided water resource managers with improved insights into multidecadal and centennial scale variability that cannot be reliably detected using shorter instrumental records. Paleoclimate streamflow reconstructions have largely focused on individual catchments limiting the ability to quantify variability across the CONUS. The Living Blended Drought Atlas (LBDA), a spatially and temporally complete 555 year long paleoclimate record of summer drought across the CONUS, provides an opportunity to reconstruct and characterize streamflow variability at a continental scale. We explore the validity of the first paleoreconstructions of streamflow that span the CONUS informed by the LBDA targeting a set of U.S. Geological Survey streamflow sites. The reconstructions are skillful under cross validation across most of the country, but the variance explained is generally low. Spatial and temporal structures of streamflow variability are analyzed using hierarchical clustering, principal component analysis, and wavelet analyses. Nine spatially coherent clusters are identified. The reconstructions show signals of contemporary droughts such as the Dust Bowl (1930s) and 1950s droughts. Decadal‐scale variability was detected in the late 1900s in the western U.S., however, similar modes of temporal variability were rarely present prior to the 1950s. The twentieth century featured longer wet spells and shorter dry spells compared with the preceding 450 years. Streamflows in the Pacific Northwest and Northeast are negatively correlated with the central U.S. suggesting the potential to mitigate some drought impacts by balancing economic activities and insurance pools across these regions during major droughts.

The Spatiotemporal Characteristics of Extreme Precipitation Events in the Western United States

Changes in the frequency or intensity of extreme precipitation events would have profound impacts on both human society and the natural environment. In this paper, we present the results of a comprehensive analysis of the spatiotemporal changes of extreme precipitation in the western United States. The analyses explore the spatial characterization of the El Nino-Southern Oscillation (ENSO)-extreme precipitation response pattern and identify the multi-scale temporal variability in precipitation extremes in the western United States. Results indicate: (1) Extreme precipitation expressed in indices such as seasonal count of days when precipitation is large than 10 mm (R10), seasonal maximum 5-day precipitation (R5D), maximum length of dry spell (CDD), and seasonal total precipitation exceeding 95 percentile (R95) have a dipolar pattern and a transition zone which separates the west into two main dipolar centers regarded as Pacific Northwest and Desert Southwest. The simple precipitation intensity index (SDII) has little correlation with large scale natural oscillations over most of the west. (2) The spatial distributions of annual trend of R10, R5D, SDII, and R95 have seasonal variability in southern California and Lower Colorado River Basin. (3) There are consistent multi-year bands ranging from 2 to 20 years in the R10, R5D, CDD, and R95 winter time series which may be caused by the inter-decadal or multi-decadal modulation of ENSO effects on precipitation extremes. The results can provide beneficial reference to the prediction of precipitation extremes in the west.

Pacific and Atlantic Ocean influence on the spatiotemporal variability of heavy precipitation in the western United States

In this study, we test our hypothesis that no single index such as El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multi-decadal Oscillation (AMO) or North Atlantic Oscillation (NAO) derived from the Pacific and Atlantic Oceans can explain the multi-scale temporal variability and spatial distribution of heavy precipitation in the western United States. Instead, it may be possible to utilize a characterization of their integrated effect or some other unidentified factors which reflects the combined physical oceanic–atmospheric processes that occur. For this purpose, Empirical orthogonal function (EOF) analysis is performed on summer (April–September) and winter (October–March) heavy precipitation expressed as total precipitation when daily precipitation is larger than 95th percentile (R95) to indentify the leading modes of variability during the period 1948–2009. The correlation between the principle components (PCs) of each EOF mode with Sea Surface Temperature (SST) anomalies is evaluated. The analysis has shown that the leading modes of R95 variability and the connections between local R95 and SST over western United States are seasonally dependent. The first EOF mode of summer R95 is associated with AMO. The first two EOF modes of winter R95 are related to an integrated effects of ENSO, PDO, and NAO which explain nearly half (49%) of the spatial and temporal variance in R95 in this region. Additionally, the coupled effects of these three oceanic–atmospheric oscillations on winter R95 are evaluated by investigating the ENSO-R95 responses modulated by a combination of different PDO and NAO phases. Based on our analyses and predicted future states of these oceanic–atmospheric oscillations, we suggest possible heavy precipitation scenarios for upcoming decades which may be useful to forecasters and water managers.

How well do the GCMs/RCMs capture the multi-scale temporal variability of precipitation in the Southwestern United States?

Summary Multi-scale temporal variability of precipitation has an established relationship with floods and droughts. In this paper, we present the diagnostics on the ability of 16 General Circulation Models (GCMs) from Bias Corrected and Downscaled (BCSD) World Climate Research Program’s (WCRP’s) Coupled Model Inter-comparison Project Phase 3 (CMIP3) projections and 10 Regional Climate Models (RCMs) that participated in the North American Regional Climate Change Assessment Program (NARCCAP) to represent multi-scale temporal variability determined from the observed station data. Four regions (Los Angeles, Las Vegas, Tucson, and Cimarron) in the Southwest United States are selected as they represent four different precipitation regions classified by clustering method. We investigate how storm properties and seasonal, inter-annual, and decadal precipitation variabilities differed between GCMs/RCMs and observed records in these regions. We find that current GCMs/RCMs tend to simulate longer storm duration and lower storm intensity compared to those from observed records. Most GCMs/RCMs fail to produce the high-intensity summer storms caused by local convective heat transport associated with the summer monsoon. Both inter-annual and decadal bands are present in the GCM/RCM-simulated precipitation time series; however, these do not line up to the patterns of large-scale ocean oscillations such as El Nino/La Nina Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Our results show that the studied GCMs/RCMs can capture long-term monthly mean as the examined data is bias-corrected and downscaled, but fail to simulate the multi-scale precipitation variability including flood generating extreme events, which suggests their inadequacy for studies on floods and droughts that are strongly associated with multi-scale temporal precipitation variability.

Scaling characteristics of precipitation data in conjunction with wavelet analysis

Summary A large set of monthly precipitation data from 43 stations throughout Texas was employed to investigate the spatial variability in the multiscaling properties of wet and dry spells. Special emphasis was given to dry spells which are related to meteorological droughts. Scaling properties deduced from the analysis of dry spells can be used in drought modeling and multiscale temporal variability of droughts. Using moment scaling exponents, scaling properties of wet and dry spells were examined for a median truncation level. No coherent regional differences were found from the spatial depiction of scaling parameters. Wet and dry spells showed different tendencies in simple scaling and multiscaling throughout the study area. Also, significant low frequency patterns of precipitation were found when the wavelet transform was used. Investigation of the relationship between scaling properties and significant cycles of precipitation data showed that annual cycles may contribute to the occurrence of simple scaling mechanism in wet and dry spell sequences. Such characterization of sequences of wet and dry spells is essential for addressing a multitude of hydrological problems, including estimation of flood and drought frequencies, construction of rainfall and runoff relationships, agricultural planning, to name but a few.

Estimation of multi-scale temporal variability of air and water temperature in the northeastern part of the Black Sea

Standard deviations of mesoscale, synoptic, seasonal, and interannual fluctuations of the air and water temperature are calculated from long-term measurements. The contributions of each type of fluctuation to the total temporal variability are estimated. The intraannual cycle of variability of monthly (long-term) means of mesoscale and synoptic fluctuations is considered.

A comparative modeling analysis of multiscale temporal variability of rainfall in Australia

The effects of long‐term natural climate variability and human‐induced climate change on rainfall variability have become the focus of much concern and recent research efforts. In this paper, we present the results of a comparative analysis of observed multiscale temporal variability of rainfall in the Perth, Newcastle, and Darwin regions of Australia. This empirical and stochastic modeling analysis explores multiscale rainfall variability, i.e., ranging from short to long term, including within‐storm patterns, and intra‐annual, interannual, and interdecadal variabilities, using data taken from each of these regions. The analyses investigated how storm durations, interstorm periods, and average storm rainfall intensities differ for different climate states and demonstrated significant differences in this regard between the three selected regions. In Perth, the average storm intensity is stronger during La Niña years than during El Niño years, whereas in Newcastle and Darwin storm duration is longer during La Niña years. Increase of either storm duration or average storm intensity is the cause of higher average annual rainfall during La Niña years as compared to El Niño years. On the other hand, within‐storm variability does not differ significantly between different ENSO states in all three locations. In the case of long‐term rainfall variability, the statistical analyses indicated that in Newcastle the long‐term rainfall pattern reflects the variability of the Interdecadal Pacific Oscillation (IPO) index, whereas in Perth and Darwin the long‐term variability exhibits a step change in average annual rainfall (up in Darwin and down in Perth) which occurred around 1970. The step changes in Perth and Darwin and the switch in IPO states in Newcastle manifested differently in the three study regions in terms of changes in the annual number of rainy days or the average daily rainfall intensity or both. On the basis of these empirical data analyses, a stochastic rainfall time series model was developed that incorporates the entire range of multiscale variabilities observed in each region, including within‐storm, intra‐annual, interannual, and interdecadal variability. Such ability to characterize, model, and synthetically generate realistic time series of rainfall intensities is essential for addressing many hydrological problems, including estimation of flood and drought frequencies, pesticide risk assessment, and landslide frequencies.

An investigation of the multi-scale temporal variability of beach profiles at Duck using wavelet packet transforms

In an earlier paper a particular discrete wavelet transform (DWT) was used to study the complex variation of beach profile changes. However, use of the DWT requires that the sequence of spatial and temporal resolution is fixed as a dyadic sequence, which means that the variability over longer intervals is not characterised well. Here we introduce the discrete wavelet packet transform (DWPT) that uses an adaptive scaling to partition the data variance, according to an entropy cost function. The advantages of this approach are demonstrated by its application to the study of temporal variability of a 22 year record of beach profile data from the Field Research Facility (FRF) at Duck, North Carolina, USA. Time series of beach elevations at three locations across a particular profile are investigated in detail. We conclude that the DWPT provides a superior analysis of non-stationary time series to that of the DWT, with improved resolution of the scale intervals of the variability. The beach elevation around the shoreline is shown to respond at both sub-annual and interannual scales, but variability at an annual scale is weak. Moving seaward into deeper water, the variance is partitioned into fewer and longer scales. It is confirmed that elevation changes around the inner bar at Duck exhibit a strong interannual variation consistent with Plant et al. (Plant, N.G., Holman, R.A. and Freilich, M.H., 1999. A simple model for interannual sandbar behaviour. Journal of Geophysical Research 104(C7), 15755–15776). Around 23% of the variance around the inner bar is explained at the temporal scale of 64–128 months, which is consistent with the bar behaviour of 6 years found by Ruessink et al. (Ruessink, B. G., Wijnberg, K. M., Holman, R. A., Kuriyama, Y. and Van Enckevort, I. M. J., 2003. Intersite comparison of interannual nearshore bar behaviour. Journal of Geophysical Research, 108 (C8): 1–12). A significant new finding is, however, that about 26% of the variance is attributable to temporal scales of 16–21.3 months. Reconstruction of the wavelet packet components for individual temporal scales is shown to provide a means for identifying the impact and scale of non-stationary events, such as storms, on the beach response. This provides further information that can be used to interpret the morphological changes in terms of the forcing processes and also serves to inform morphodynamic modelling.