The last several years of research into the characterization of Earth's
hydroclimatic variability reflect, to some extent, two key facets of the
problem: (i) the continually growing availability of powerful computational
tools (along with more extensive observational records and improved analysis
techniques) for examining this variability, and (ii) the potential for
changes in this variability with changes in the global climate. Amongst the
most important modern computational tools, at least for continental- or
global-scale hydroclimatic analyses, is atmospheric reanalysis: a
mathematically optimal blending of modeling and observations that produces
complete fields in space and time of important hydrological variables (e.g.,
Kanamitsu et al., 2002; Dee et al., 2011; Bosilovich et al.,
2015; Kobayashi et al., 2015; see also
Another computational tool used heavily in the last decade for continental-
or global-scale hydrological analysis is the land data assimilation
system (LDAS), which is basically a gridded array of land model elements
driven with observation-based meteorological forcing, some of which is
derived from reanalyses. Explored early on by Dirmeyer et al. (2006), more
recent applications of the LDAS approach have benefitted from improved
global forcing datasets (e.g., Sheffield et al., 2006; Weedon et al., 2011)
and accordingly provide improved descriptions of large-scale land surface
hydrology and its variations (Reichle et al., 2011; Xia et al., 2012; Balsamo
et al., 2015). Wood et al. (2011) emphasize the importance to society of
developing hyper-resolution (
A climate model in “free-running” mode (i.e., without the assimilation of
observational data) is a computational tool with a special role in
hydroclimatic analysis, being particularly suitable for sensitivity analyses
and for analyses requiring extensive (e.g., multi-century) climate data.
Using such a model, for example, Tierney et al. (2013) show a connection
between Indian Ocean sea surface temperatures (SSTs) and east African
rainfall on multi-decadal timescales through the impact of the SSTs on the
Walker circulation. Indeed, the second topic noted above (the idea that
hydroclimatic variability is changing with time) is now largely being
addressed through sensitivity studies using such climate models. With climate
models, one can artificially modify the concentration of CO
One of the expectations of a warming climate, supported by such modeling studies (e.g., Held and Soden, 2006; Chou and Lan, 2012; Kumar et al., 2013), is that currently dry areas will get drier and wet areas will get wetter. One manifestation of such a trend is the narrowing of the Intertropical Convergence Zone (ITCZ) and the expansion of the drier subtropical area (e.g., Su et al., 2014; Lau and Kim, 2015); such a change appears to broadly resemble the observed change in the past several decades (e.g., Wilcox et al., 2012; Fu, 2015), which contributed to the shortening of both North American and South American monsoon seasons (Arias et al., 2015). However, Greve et al. (2014), upon examining multiple long-term observational datasets, conclude that the “dry gets drier, wet gets wetter” paradigm is not consistently supported by the historical data, at least over land.
Coumou and Rahmstorf (2012) cite numerous studies documenting recent rainfall and storm extremes that, taken together, suggest that greenhouse warming has affected their frequency. An observation-based analysis of global evapotranspiration fields indicates a positive trend between 1982 and 1997 that has declined thereafter (Jung et al., 2010). A similar evapotranspiration trend change in regions of North America was attributed to variability of precipitation amount (Parr et al., 2016), while Miralles et al. (2014) point to the El Niño cycle as a major control over global-scale evapotranspiration variability. Milly and Dunne (2016) warn that some estimates in the literature of increased potential evapotranspiration (PET) in a warming climate may be excessive, even those that rely on the well-considered Penman–Monteith equation for estimating PET (Monteith, 1965).
Trends in streamflow are of critical relevance to water management and have been evaluated recently (largely with historical data) in many areas (see Lorenzo-Lacruz et al., 2012, and references therein). Milly et al. (2008) argue that the historical strategy of assuming stationarity in hydrological statistics for developing water management infrastructure is no longer tenable in the face of such climatic trends. Serinaldi and Kilsby (2015), however, illustrate difficulties in using nonstationary models for the associated hydrological frequency analysis. Future climate projections suggest that the range of hydrologic variability over many locations may move completely outside the historical ranges (Dirmeyer et al., 2016).
Real-world variability, including climatic trends, was addressed by several
presentations at the symposium. Again, we summarize these presentations here
in the form of self-contained figures, with captions detailed enough to
describe the individual studies; the captions also point the reader to
relevant papers, if available, and to an appropriate contact for further
information. The six figures included in this section cover a variety of
topics:
The quantification of variability in northern
Canada streamflow indicates strong interannual and
interdecadal variability in the rivers studied (see Fig. 1), though no trend in total discharge is
observed during 1964–2013 (Déry et al., 2016). Analysis of the sources of rainfall variability over parts of Queensland,
Australia, shows that the variability is potentially
controlled more by nearby SSTs than by distant climate phenomena such as El
Niño (Fig. 2). The impact of model bias on the estimation of trends in
discharge over the coming decades is revealed when
climate projection data are applied to a default land model and to a version
of the model with improved (reduced bias) treatments of evapotranspiration
and dynamic vegetation; the two models produce contrasting trends in
streamflow associated with future drought (Fig. 3). Properly accounting for vegetation response to meteorological and hydrological
variables and for feedbacks with these variables is seen to have important implications
for the overall characterization of hydrological (streamflow) variability in a changing climate (Fig. 4). Analysis of output from state-of-the-art atmospheric models shows them to have
an equatorward bias in their positioning of the jet stream, with consequent impacts
on their simulation of atmospheric rivers and associated cold season precipitation
(Fig. 5). Improved atmospheric simulation of the jet stream may be possible with higher-resolution models. Globally distributed estimates of runoff generation may improve with a new
computational approach emphasizing calibration with remotely sensed data and keyed
to certain dominant landscape processes (Fig. 6). The approach also permits studies
of how root zone storage capacity, for example, may respond to climate variations.
Climatic change may manifest itself as changes in the statistics of
streamflow, and such changes can have important implications for water
resource management. A recent study searched for trends in the streamflow
within six basins of northern Canada; results are shown above. Each box
represents a specific basin:
It is widely assumed that large-scale SST patterns (the El Niño/La Niña patterns, for example) have an important impact on rainfall variability in regions like Australia. More proximate SSTs, however, may be just as important. This was investigated through a comparison of two 40-member ensembles of the Weather Research and Forecasting model (WRF) regional model simulations, the first using observed SSTs and the second using SSTs associated with previous La Niña events. Both ensembles employed the same atmospheric forcing along the WRF model's lateral boundary. Shown in the plot is the inferred contribution of local SSTs to the major flooding that occurred between 10 and 20 December 2010 in Queensland, Australia. In many places, the high local SSTs (within a few hundred kilometers of the coast) accounted for more of the precipitation than did the prevailing La Niña conditions, at least at the spatial scales considered here. The analysis demonstrates limitations in hydrological predictability based solely on large-scale climate modes such as El Niño/La Niña. Controls on hydrological variability and predictability are in fact more complex. (Contact: Jason Evans. See Evans and Boyer-Souchet (2012) for further information.)
Changes in climate in the coming decades will presumably be accompanied by changes in hydrological behavior at the Earth's surface – changes in the character, for example, of streamflow. Our estimates of such changes, however, may be severely limited by biases in the models used to quantify them. This is demonstrated here with two simulations of hydrological behavior in the Connecticut River basin, one using the default VIC model and the other using a version of VIC with bias-corrected evapotranspiration (VICET). The VICET model overwrites the model-estimated ET components from VIC with bias-corrected values, and such correction propagates to improve the estimation of other hydrological variables. The meteorological forcing for the two simulations is identical, which for the historical segment was derived from NLDAS-2 (Xia et al., 2012) and for the future segment was constructed based on bias correction of the NARCCAP projection following the approach of Ahmed et al. (2013) using NLDAS-2 as the observational reference. Shown in the plot, for each simulation and for both time periods, are the 5-day minimum discharges at the Thompsonville station (in cubic feet per second). The strong model dependence in the hydrological projections indicates a strong need for careful evaluation and improvement of land model parameterizations. (Contact: Guiling Wang. See Parr et al. (2015) for further information.)
The characterization of hydrological changes associated with climate change requires a consideration of vegetation disturbance, as indicated by a number of simulations of San Juan River basin streamflow with the variable infiltration capacity (VIC) model. Several simulations are considered here: one using historical (1970–1999) meteorological forcing (average streamflow shown as a thick black line) and others using future (2070–2099) temperature and precipitation forcing from the Intergovernmental Panel on Climate Change (IPCC) CMIP5 database (four different sets of forcing from four different Earth system models, or ESMs). Future streamflow conditions are provided for two vegetation disturbance scenarios. The thin black line (with gray shading underneath) represents the average seasonal cycle of simulated streamflow from future runs which utilize the historical representation of vegetation. The green envelope (mean is shown as a dashed green line), on the other hand, represents the range of average seasonal cycles produced in future runs (one for each of the four ESMs) that results from the imposed forest mortality of close to 90 % by the 2080s, based on work from McDowell et al. (2016). We see that for the San Juan River basin, a major tributary to the Colorado River basin, spring freshet in the future runs occurs earlier in the season, shifting from mid-May to the end of April. Flows are projected to be higher during late fall, winter, and early spring, and lower during late spring, summer, and early fall. Disturbing the vegetation in addition to using projected temperature and precipitation forcing results in a different pattern of streamflow, with lower flows in early spring and then higher peak flow, and with lower recessional summer flows due to differences in how regrowth vegetation (i.e., shrubs) partitions water and snowpack. Studies on climate change thus require a consideration of changes in vegetation dynamics; otherwise results may be misleading or could underestimate impacts. (Contact: Katrina Bennett.)
Atmospheric rivers (ARs) are responsible for over 90 % of the
moisture transport to the extratropics (Zhu and Newell, 1998). They also
contribute significantly to heavy precipitation and flooding in many regions
worldwide (Ralph et al., 2006). Understanding how ARs may change in a warmer
climate is important for managing water resources and flood risk. Associated
with Rossby wave breaking, the frequency of ARs and their landfall locations
are influenced by the jet stream. Global climate models in the Coupled Model
Intercomparison Phase 5 (CMIP5) exhibit an equatorward bias in the simulated
jet position. For example, panel
Readily available remote sensing products can be used to constrain
hydrological models in a way that allows streamflow prediction in ungauged
basins. The above schematic shows the relevant connections to consider during
a calibration procedure. HAND refers to the height above the nearest drainage
(which is the hydraulic head); root zone storage capacity is the maximum
amount of soil water that can be accessed by the vegetation root systems;
the recession timescale parameter controls the steepness of the recession.
Naturally, a different group of attendees would have provided a different sampling of research. This particular sampling, however, can be considered representative, indicative of the wide variety of topics now being addressed in the area of general hydroclimatic variability and trends.
Given its societal relevance, drought has been tracked extensively in recent
years. In the United States, the US Drought Monitor (
Along with new measurement approaches come improved statistical and modeling treatments of drought, as reviewed by Mishra and Singh (2011). A Bayesian approach was recently applied by Kam et al. (2014) to connect drought probability to phases of the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and El Niño–Southern Oscillation (ENSO) cycles. Pan et al. (2013) use a copula (joint probability distribution) approach focusing on a soil moisture-based drought index and precipitation forecasts to characterize uncertainties in drought recovery. Land surface modeling in combination with observations of meteorological forcing provides a unique means for monitoring drought on the global scale (e.g., Nijssen et al., 2014). Numerical climate models have evolved substantially in the last decades, and their application to drought studies is growing; Hoerling et al. (2014), for example, use such models to analyze the 2012 United States Great Plains drought, and Coats et al. (2015) evaluate their ability to reproduce the character of paleoclimatic megadroughts in southwest North America.
The specter of climate change largely manifests itself in concerns that drought frequency will increase. Numerical model simulations of changing climate provide much of the needed data for focused study; Seager and Vecchi (2010) use these models to examine the character of future drought in southwestern North America, concluding that the occurrence of drought there can be expected to increase in the coming century due to reduced precipitation from large-scale atmospheric circulation changes during winter months. Cook et al. (2014) examine climate model simulations to quantify the relative impacts on agricultural drought of changes in precipitation and temperature (through evapotranspiration) and demonstrate that the temperature impact is substantial. Dai (2013) evaluates the historical record and climate change simulations in the context of aridity changes and concludes that the models are generally consistent with the historical record up to 2010. Regarding California drought, Mao et al. (2015) studied the historical record (rather than climate simulations) and conclude that the 2013–2014 drought was induced by reduced precipitation rather than by the observed temperatures trend, while Diffenbaugh et al. (2015) find that reduced precipitation in California is more likely during anomalously warm years. Mo and Lettenmaier (2015) find that flash drought, based on a definition of concurrent heat extreme, soil moisture deficit, and evapotranspiration (ET) enhancement, has been in decline over the US during the last 100 years (though with a rebound after 2011), while recent work by Wang et al. (2016) indicates that the occurrence of flash drought in China has doubled during the past 30 years. A severe flash drought in the summer of 2013, for example, ravaged 13 provinces in southern China. Trenberth et al. (2013) highlight some of the difficulties associated with characterizing changes in drought behavior over time, pointing to deficiencies in the precipitation datasets being used and to the need to account properly for sources of natural variability, such as ENSO.
Joint analysis of a variety of climate variables provides new
insights into the predictability of seasonal drought in China and into recent
changes in the character of flash drought there. The top panels show
Given its importance, drought has been the subject of several recent overview and review papers; the interested reader is directed to these papers for further information. Mishra and Singh (2010) describe drought definitions and drought indices and identify important gaps in drought research. Wood et al. (2015) provide a synthesis of research (largely focused on North American drought) performed by the National Oceanographic and Atmospheric Administration's Drought Task Force, and Schubert et al. (2016) review the latest understanding of meteorological drought as it manifests itself around the world. Kiem at al. (2016) reviews the current understanding and history of drought in the Australian context, including implications for future droughts given climate change. Peterson et al. (2013), in their overview of droughts in the United States, provide additional useful references.
The possibility that soil moisture anomalies can affect the
character of the overlying atmospheric circulation could have profound
implications for our understanding of drought evolution and maintenance. The
plot above shows the statistical connection between soil moisture (as derived
from offline land analyses) and 500 hPa geopotential height anomalies (as
derived from an atmospheric reanalysis). More specifically, the red curve
shows the lead–lag correlation between pentad soil moisture anomalies and
the height anomalies during May–July (MJJ) over the south-central United
States over the period 1981–2012, whereas the blue line depicts the
autocorrelation function (ACF) of the pentad 500 hPa geopotential height
anomalies of MJJ for the same region and period. The ACF values have been
multiplied by
The symposium included two presentations that focused specifically on
drought mechanics and drought character:
The first presentation discussed drought in China (Fig. 7). Drivers of seasonal (summertime) meteorological
drought in northern China include the El Niño cycle and springtime
Eurasian snow cover; in southern China, the probability of flash drought
appears to be increasing. The second presentation dealt with the impact of soil moisture on the atmospheric general circulation (Fig. 8).
Observed connections between soil moisture, clouds, convection, and
subsidence may underlie a mechanism by which soil moisture influences not
only local rainfall but also the large-scale atmospheric circulation in
such a way as to sustain dry anomalies from spring to summer.
Both of these presentations address mechanisms that may contribute to improved seasonal predictions of drought.
Much of the recent research has addressed flash floods in Europe. Gaume et al. (2009), for example, describe their compilation of nearly 600 flash flood events in Europe, and Marchi et al. (2010) characterize European flash floods in the context of basin morphology, rainfall characteristics, antecedent soil moisture, and other factors. An extensive field experiment aimed at quantifying facets of flash floods in the northwestern Mediterranean was conducted in the fall of 2012 (Ducrocq et al., 2014). The nature of floods has been studied in other areas as well; Gochis et al. (2015) analyze the meteorological and hydrological conditions underlying the September 2013 Colorado flood event in great detail, addressing forecast capabilities and also pointing to new observations that may help prepare for future events. Berghuijs et al. (2016) examine the mechanisms underlying flood generation in the continental US and find that precipitation in isolation is not a good predictor of maximum annual flow; precipitation needs to be considered in conjunction with soil moisture and snow amounts. Teufel et al. (2017) perform a meteorological analysis of the June 2013 Alberta floods. Huang et al. (2014) used a combination of ground-based and satellite data to map flood inundation in the Murray–Darling Basin of Australia.
Many recent studies have addressed potential changes in flood character associated with changes in climate. Mallakpour and Villarini (2015) examine the observational record in the central United States and find an increase in the frequency of flood events there, though not an increase in the largest flood peaks. Regarding future changes, Hirabayashi et al. (2013) combine climate change projections from a number of climate models with a global river routing model to determine that regions such as southeast Asia and eastern Africa may be subject to greater flood frequency by the end of the century. Similarly, Arnell and Gosling (2016) ingest the results of climate projections from multiple climate models into a global hydrological model and, considering impacts on future distributions of human population, find indications of increased flood risk, though the magnitudes of the impacts are uncertain given the variability in the projections. Hallegatte et al. (2013) address the costs of flooding in coastal cities, which are especially prone to the effects of subsidence and sea level rise.
Scientific progress in conjunction with advances in web-based software technologies are providing society with valuable new tools for coping with the physical and economic uncertainties associated with flooding. The above screenshot, for example, is from the Aqueduct Global Flood Analyzer, a web-based interactive platform that estimates river flood risk in terms of urban damage, affected gross domestic product (GDP), and affected population at the country, state, and river basin scale across the globe. The analyzer enables users to estimate current flood risk for a specific geographic unit, taking into account existing local flood protection levels. It also allows users to project future flood risk under climate and socioeconomic change and separately attribute change in flood risk to each of these drivers. Finally, for each flood protection level, high-resolution maps of yearly flooding probability are provided. The basis for the analyzer is the global hydrology and water resources model PCR-GLOBWB (Van Beek et al., 2011). The methodology behind the tool is described extensively in Ward et al. (2013) and Winsemius et al. (2016). Current developments for this tool entail adding the risk of coastal flooding and analyzing the costs and benefits of adaptation measures, including traditional “hard defenses” and nature-based solutions. (Adapted from Bierkens, 2015. Contact: Marc Bierkens)
In nature, changes in the storage of water in a hydrological basin
can smooth out hydrological variations associated with floods and droughts.
The spatial variability in necessary hydrological storage, however, remains
relatively unstudied – at the present time there is no global map showing
the storage needed to ameliorate floods and droughts, either for the present
climate or under climate change. Using the
Ganges–Brahmaputra–Meghna Basin as an example, the needed storage at each
grid cell within the basin is calculated with a new method:
intensity–duration–frequency curves of flood and drought (flood duration
curve and drought duration curve: FDC-DDC, an alternative representation of
discharge time series obtained from a calibrated hydrological model called
BTOPMC – see Takeuchi and Masood, 2016). For simplicity, the target release
(
Hall et al. (2014), citing many recent studies, provide a thorough review of flood regime changes inferred in Europe based on observations and model experiments. Johnson et al. (2016) provide a review of historical trends and variability of floods in Australia, along with an assessment of future flood hazards given climate change. Kundzewicz et al. (2014) offer a global look at flood potential in the context of climate change and indicate a low level of confidence in current projections of the character (magnitude and frequency) of floods.
Several presentations at the symposium focused on floods and flooding; two
are represented here:
The first focused on flood monitoring and forecasting. A system known as the
Aqueduct Global Flood Analyzer estimates flood risks across the globe, considering
aspects such as flood hazard, exposure, and vulnerability (Fig. 9). The second addressed the joint analysis of flood and drought potential.
Floods and droughts need to be considered together in reservoir design and operation.
Their joint impacts vary spatially, leading to global variations in the relative
difficulty of managing hydrological variability (Fig. 10).
Flood monitoring and forecasting systems are indeed important sources of information for mitigating the societal impacts of floods. The first example is one of a number of such systems described at the symposium.
An important facet of climate science is the idea that the land surface is an active, dynamic component of the climate system rather than simply a passive respondent – especially the idea that soil moisture variations can imprint themselves on the overlying meteorology and on associated hydrological variability. Seneviratne et al. (2010) provide an extensive overview of research into the nature of this land–atmosphere coupling. The continuing research is shedding new light on the ability of soil moisture to influence, for example, rain variability and heat waves.
The soil moisture–air temperature connection is intuitive; drier soils evaporate less and thus experience less evaporative cooling, leading to higher temperatures for the local system. This connection has been examined, for example, in the context of the 2003 European heat wave (Fischer et al., 2007). More difficult to pin down is the soil moisture–precipitation connection. Indeed, the literature indicates complexities regarding the directions of the feedback, i.e., in whether increased soil moisture leads to increased or decreased rainfall. For example, Findell et al. (2011) find that over the eastern United States, increased soil moisture leads to a greater probability of afternoon rainfall, supporting the idea of positive feedback, whereas Taylor et al. (2012) provide observational evidence that rainfall tends to fall over the drier patches in a landscape. Guillod et al. (2015) address the apparent contradiction by showing that large-scale wet conditions are in general favorable to increased precipitation (a positive temporal correlation at the large scale), yet rainfall can favor the drier patches within the broadly wet conditions (a negative spatial correlation). Theory suggests that some atmospheric conditions promote a positive soil moisture–rainfall feedback, whereas others promote a negative one; Ferguson and Wood (2011), through an analysis of satellite-based data, separate the globe into the associated different coupling regimes, and Roundy et al. (2013) extend the methodology to show how the coupling regime in a given location can change with time.
Naturally, land–atmosphere coupling has been studied extensively within climate models. One recent study (Saini et al., 2016) examines past drought events using a regional climate model with different soil moisture initializations; soil moisture feedback is found to be much more important for the development of the 2012 drought in the central US than for the development of the 1988 drought there, due to the lack in 2012 of a clear large-scale forcing favoring drought. Using a different model, Koster et al. (2016) show that soil moisture deficits in the interior of North America can help generate atmospheric circulation patterns that in turn can contribute to the persistence and areal expansion of the dryness. Regarding the impact of climate change on land–atmosphere coupling, Dirmeyer et al. (2013a, b, 2014b) analyze the water cycle in CMIP5 models in several ways, noting evidence for enhanced land–atmosphere feedbacks in a changing climate arising in concert with increasing extremes. Worth noting, though, is that models with parameterized convection may have difficulty in properly representing land–atmosphere coupling. Recent advances in convection-permitting modeling may lead to better simulations of convection and land–atmosphere interactions (e.g., Hohennegger et al., 2009; Leung and Gao, 2016).
Some recent work has advocated a more holistic treatment of land–atmosphere coupling, one that considers the co-evolution of snow properties, cloud forcing, temperature, relative humidity, precipitation, wind, and boundary layer growth. On the Canadian Prairies, for example, the monthly variability of temperature and relative humidity in the warm season is dominated by shortwave cloud forcing, and as a result, both equivalent potential temperature and the lifting condensation level, which drive moist convective development, depend strongly on cloud forcing (Betts et al., 2013a, 2015). This has implications for seasonal predictability, given the uncertainties in predicting daily cloud forcing in numerical forecast models. Betts et al. (2017) provide a set of coupling coefficients between the near-surface diurnal cycle of the moist thermodynamic variables, cloud forcing, and lagged precipitation for model evaluation. Another challenge for seasonal predictability is the dynamic coupling between vegetation phenology, precipitation anomalies, soil water extraction, and evapotranspiration. The intensification of cropping increases evapotranspiration and cools the summer climate both in the Midwestern US (Mueller et al., 2016) and the Canadian Prairies (Betts et al., 2013b), and the extraction of soil water during the growing season appears to dampen precipitation anomalies (Betts et al., 2014b) and perhaps contributed to the onset of the 2012 Great Plains drought (Sun et al., 2015).
Land surface hydrological processes and atmospheric (boundary layer)
processes do not proceed in isolation from each other; land states and
boundary layer states evolve together, as a joint system. The nature of this
coupled system was recently elucidated through a careful analysis of a wealth
of land surface and boundary layer data collected by trained observers in the
Canadian Prairies. These observers recorded hourly, since 1953, the fraction
of the sky covered by opaque reflective cloud, providing daily shortwave and
longwave cloud forcing (SWCF and LWCF) on climate timescales when calibrated
against baseline surface radiation measurements (Betts et al., 2015). The
panels above express some of the important relationships inherent in these
data in the form of average diurnal temperature cycles for
January
The Global Land Atmosphere System Study (GLASS) panel of the Global Energy
and Water Exchanges (GEWEX) project has focused recently on the definition
and evaluation of land–atmosphere coupling processes in models and
observational data (Santanello et al., 2011) with a particular focus on the
hydrologic cycle. The reader is directed to the website
Symposium papers addressed several facets of land–atmosphere coupling,
including the attribution of the sources of the coupling strength simulated
by an Earth system model and the evaluation of simulated coupling
characteristics with relevant observational datasets. One of these
presentations is represented here:
Joint analysis of surface and boundary layer data from an
extensive dataset collected over the Canadian Prairies, in the context of the
aforementioned holistic approach to analyzing land–atmosphere interaction,
reveals important connections between cloud radiative forcing and
near-surface air temperature, including how these connections change in the
presence of snow cover (Fig. 11).
Again, a key motivation for studying hydroclimatic variability is improvement in the skill of hydrological predictions – skillful predictions can allow society to prepare itself better for upcoming hydrological variations. One highly relevant tool for this is the extended-range forecast system, a coupled ocean–atmosphere–land modeling system that provides, among other things, forecasts of temperature and rainfall over continents weeks to months in advance. Doblas-Reyes et al. (2013) provide a review of the state of the art in seasonal forecasting with such systems, Yuan et al. (2015) provide a review of climate model-based seasonal hydrological forecasting, and Robertson et al. (2015) and Vitart et al. (2017) describe emerging operational subseasonal-to-seasonal (S2S) forecast systems. Regarding the overall accuracy of seasonal forecasts, Roundy and Wood (2015) use statistical models to examine how such forecasts may be limited by biases in their treatment of land–atmosphere coupling, and Yuan and Wood (2012) address critical questions regarding the combination of forecasts from different systems – whether redundancies amongst the systems can be properly accounted for when developing a multi-model forecast.
In essence, forecast skill in a subseasonal-to-seasonal forecast system is derived from the information content inherent in the system's initialization. Therefore, considerable effort has been directed toward improving this initialization, for example, through the improvement of Bayesian (Kalman and particle filters) and variational (1D–4D) data assimilation methods as applied to the initialization of high-dimensional models (e.g., Li et al., 2015; van Leeuwen, 2015). A promising strategy is based on combining advantageous characteristics of both paradigms (e.g., the probabilistic estimates for Bayesian methods and the broader evaluation window for variational ones), as demonstrated by, for example, Buehner et al. (2010) and Noh et al. (2011).
While the initialization of ocean states has long been considered key for the coupled forecast systems (NRC, 2010), there is growing recognition that the initialization of various land states may be just as critical to extracting otherwise unattainable facets of skill (e.g., Dirmeyer and Halder, 2017). Soil moisture impacts on subseasonal forecast skill are quantified across a broad range of systems in the Global Land-Atmosphere Coupling Project (Koster et al., 2011; van den Hurk et al., 2011); impacts are found to be much larger on temperature forecast skill, but impacts on precipitation forecast skill are significant in places, particularly when considering the strongest initial soil moisture anomalies. A positive impact of snow initialization on seasonal temperature forecast skill is demonstrated by Peings et al. (2011) and Lin et al. (2016); the latter show that the assimilation of satellite measurements improves the initialization, with concomitant impacts on the forecast skill. Koster and Walker (2015) show that when a dynamic plant phenology model is used in a forecast system, initializing the vegetation state (e.g., the leaf area index) has a positive impact on temperature forecasts but not on precipitation forecasts. Subsurface temperature is another variable to consider; Xue et al. (2016) demonstrate that initializing these temperatures in an atmospheric modeling system can improve the simulation of subsequent drought. As shown by Dirmeyer et al. (2013c), the predictability of meteorological variables (the theoretical maximum forecast skill that can be derived from an initialization) may change as the climate changes.
Drought forecasting in particular has been a focus of much recent work. In sub-Saharan Africa, an advanced drought monitoring and forecasting system based on hydrological modeling, remote sensing, and seasonal forecasts has been developed and implemented, for example, at regional weather and climate centers in Niger and Kenya (Sheffield et al., 2014). Regarding the skill of seasonal drought forecasts, results are mixed. Yuan and Wood (2013), in an analysis of multiple seasonal forecast systems, uncover significant limitations in the ability of such systems to forecast drought. Quan et al. (2012), however, using a specific seasonal forecast system, demonstrate that the sea surface temperatures produced in the system, particularly those associated with El Niño cycles, add some skill to drought prediction over the United States. Roundy et al. (2014) demonstrate that apparent deficiencies in the simulated land–atmosphere coupling behavior of a forecast system can limit its ability to predict and maintain drought.
The success of hydrological prediction depends largely on the
accuracy of the initialization of the forecast model. Advanced mathematical
tools (i.e., data assimilation algorithms) are now available to transform a
given set of observations into the best forecast initialization possible. The
table above outlines the features of three data assimilation approaches:
standard Bayesian data assimilation algorithms (KF stands for Kalman filter,
EnKF stands for ensemble Kalman filter, and PF stands for particle filter),
variational methods, and a new technique – Optimized PareTo Inverse Modeling
through Integrated Stochastic Search (OPTIMISTS) – that combines the
advantageous characteristics of the first two. Some of the features selected
for OPTIMISTS, such as non-Gaussian probabilistic estimation and support for
non-linear model dynamics, are considered advantageous in the literature (van
Leeuwen, 2015); flexible configurations are available for other features
(e.g., the choice of optimization objectives or the analysis time step) for
which no consensus has formed. In the bottom panel, different configurations
of OPTIMISTS (indicated along
Streamflow forecasting has obvious relevance to water resources management, and relative to drought forecasting, it can rely less on dynamical seasonal forecasts given the strong connection between streamflow and, for example, snow storage at the start of a forecast period. Koster et al. (2010) and Mahanama et al. (2012), without using a dynamical forecast model, produce accurate streamflow forecasts at seasonal lead times based solely on initial snow and soil moisture information. This said, seasonal climate forecasts (perhaps combined with medium-range weather forecasts, as described by Yuan et al., 2014) can add skill to long-term streamflow forecasts (Yuan et al., 2013).
If, in the real world, land surface variations (e.g., in soil
moisture) are able to affect the overlying atmosphere, and if an atmospheric
model does not capture adequately this land–atmosphere feedback, the
performance of the model will suffer. A forecast model that lacks this
feedback likely cannot translate the information contained in soil moisture
states into improved forecasts of air temperature and precipitation. With
this as motivation, the panels above provide an evaluation of
land–atmosphere feedback in the US operational forecast model (CFSv2). The
three columns show from left to right the pair-wise correlations (i) between
monthly CFSv2 reforecast precipitation (
Demargne et al. (2014) describe in detail the operational Hydrologic Ensemble Forecast Service, which provides, through integration of multiple inputs (including meteorological forecasts), streamflow forecasts at leads from 6 h to 1 year. Pagano et al. (2014) outline the challenges faced by forecast agencies around the world in developing an operational river forecasting system that is suitably effective.
Two symposium presentations focusing on
hydrological prediction and forecasts are represented here.
A data assimilation approach for forecast initialization called OPTIMISTS
(Optimized PareTo Inverse Modeling through Integrated Stochastic Search) combines
features from Bayesian and variational methods for the initialization of highly
distributed hydrological models (Fig. 12). The idea that the US operational forecast model underestimates land–atmosphere
coupling is inferred from the fact that observed precipitation rates are more closely
related to antecedent soil moisture than are model-simulated rates (Fig. 13).
Of course, improved hydrological prediction is an “end goal” of much of today's hydrological research. Prediction is thus an important subtheme of many of the other examples provided in this paper.