Gridded Data Products Research Articles

New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid

AbstractIn applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use

Abstract. Population data represent an essential component in studies focusing on human–nature interrelationships, disaster risk assessment and environmental health. Several recent efforts have produced global- and continental-extent gridded population data which are becoming increasingly popular among various research communities. However, these data products, which are of very different characteristics and based on different modeling assumptions, have never been systematically reviewed and compared, which may impede their appropriate use. This article fills this gap and presents, compares and discusses a set of large-scale (global and continental) gridded datasets representing population counts or densities. It focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses. Written by the data producers and members of the user community, through the lens of the “fitness for use” concept, the aim of this paper is to provide potential data users with the knowledge base needed to make informed decisions about the appropriateness of the data products available in relation to the target application and for critical analysis.

Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations

Abstract. Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).

An enigmatic decoupling between heat stress and coral bleaching on the Great Barrier Reef.

Ocean warming threatens the functioning of coral reef ecosystems by inducing mass coral bleaching and mortality events. The link between temperature and coral bleaching is now well-established based on observations that mass bleaching events usually occur when seawater temperatures are anomalously high. However, times of high heat stress but without coral bleaching are equally important because they can inform an understanding of factors that regulate temperature-induced bleaching. Here, we investigate the absence of mass coral bleaching on the Great Barrier Reef (GBR) during austral summer 2004. Using four gridded sea surface temperature data products, validated with in situ temperature loggers, we demonstrate that the summer of 2004 was among the warmest summers of the satellite era (1982–2017) on the GBR. At least half of the GBR experienced temperatures that were high enough to initiate bleaching in other years, yet mass bleaching was not reported during 2004. The absence of bleaching is not fully explained by wind speed or cloud cover. Rather, 2004 is clearly differentiated from bleaching years by the slow speed of the East Australian Current (EAC) offshore of the GBR. An anomalously slow EAC during summer 2004 may have dampened the upwelling of nutrient-rich waters onto the GBR shelf, potentially mitigating bleaching due to the lower susceptibility of corals to heat stress in low-nutrient conditions. Although other factors such as irradiance or acclimatization may have played a role in the absence of mass bleaching, 2004 remains a key case study for demonstrating the dynamic nature of coral responses to marine heatwaves.

A systems approach to routing global gridded runoff through local high-resolution stream networks for flood early warning systems

Global or national scale flood early warning systems (FEWS) can benefit developing countries and ungauged regions that lack observational data, computational infrastructure, and/or the human capacity for streamflow modelling. Existing land surface models (LSM) typically generate forecasts using coarse resolution grid cells which, at least for streamflow, have little value when used for flood warning at local scales. We present the design and development of a new automated computational system, using existing, well-established open source software tools that quickly downscales (or maps) the runoff generated from such coarse grid-based LSMs onto high-resolution vector-based stream networks then routes the results using a vector-based river routing model. We conducted experiments using the ERA-Interim/Land reanalysis data – a 35-year retrospective gridded runoff data product from the European Center for Medium-range Weather Forecasts (ECMWF) – to assess our fast downscaling system. The accuracy of our approach is comparable to the Global Flood Awareness System (GloFAS) – a well-established gridded routing model using the same forcings – but our method provides streamflow predictions on significantly higher resolution stream networks. We found that the river network resolution has negligible effect on the simulated streamflow with our model routing. In other words, we can forecast streamflow for very small stream segments and potentially improve local flood awareness and response much more successfully than previously possible using readily available climate forcings from LSMs.

Catchment specific evaluation of Aphrodite’s and TRMM derived gridded precipitation data products for predicting runoff in a semi gauged watershed of Tropical India

Data scarcity poses difficulties to rainfall-runoff modelling in a semi/un-gauged river basin. There are certain alternative precipitation data sources, including satellite derived and gauge data derived interpolated products. However, their efficiency in estimating runoff is questioned and proved to be condition specific. In this article, an attempt has been made to compare two mostly used gridded precipitation data products, i.e. TRMM and Aphrodites in predicting runoff for semigauged Kangshabati river basin of tropical part of India during three consecutive years using a simplistic NRCS-CN approach. Considering the purpose of comparing the results in an uncalibrated model setting was used and performance of the outputs of runoff simulations based on both the precipitation datasets were tested using coefficient of determination, RMSE and Nash–Sutcliffe methods. Catchment wise model performance was compared to understand the relationship pattern of the model and input data. The accuracy level of the model outputs for upper, middle and lower catchments using two different types of input precipitation data is distinguishable. Comparing the discharge magnitude as predicted by two simulations using statistical parameters, it is apparent that the accuracy level of TRMM based discharge prediction is more in upper catchment and Aphrodite based discharge prediction is more in lower catchment. In other words, TRMM is more effective than Aphrodite’s in recording higher precipitation or high intensity discharge than that of lower precipitation or low intensity discharge and vice versa.

A Reassessment of North American River Basin Cool‐Season Precipitation: Developments From a New Mountain Climatology Data Set

AbstractCharacterizing hydrological processes on large scales is challenging due to limitations of observational networks, remoting sensing platforms, and modeling techniques. Water balances have larger uncertainties in mountain regions, where orographic processes produce high spatial variability in precipitation patterns and snow accumulation. Recent work suggests current water budgets underestimate mountain snow water storage, perhaps indicating biases in modeled precipitation. We assess whether global hydroclimate data sets underestimate precipitation for six North American watersheds, ranging from 3–70% mountainous. By selecting a single representative year for each watershed, we compare relatively high‐resolution precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model with four global products: Modern‐Era Retrospective Analysis for Research and Applications, version 2, the Global Land Data Assimilation System, the Global Precipitation Climatology Project, and the Climate Research Unit's climate data set. Comparisons to WRF precipitation suggest that observation‐based gridded data products do not produce reasonable estimates of watershed‐scale cool‐season precipitation, underestimating by 1–69%. The Global Precipitation Climatology Project and the Climate Research Unit data set have average biases of −26% and −38%, respectively. The Modern‐Era Retrospective Analysis for Research and Applications version 2 and the Global Land Data Assimilation System show smaller underestimates relative to WRF (−17% and −21%, respectively), with nearly all mean bias from the mountains (underestimated by 27% and 39%) rather than the topographically simpler lowlands (underestimated by 5% and 2%). We suggest global products fail to capture orographic enhancement of precipitation, resulting in large underestimates of precipitation, snowfall, and snow water storage in mountains of selected North American watersheds, which highlights the need for more accurate precipitation estimates to accurately assess spatiotemporal variations in the water cycle.

Global ocean heat transport dominated by heat export from the tropical Pacific

Heat redistribution is one of the main mechanisms by which oceans regulate Earth’s climate. Analyses of ocean heat transport tend to emphasize global-scale seawater pathways and concepts such as the great ocean conveyor belt. However, it is the divergence or convergence of heat transport within an oceanic region, rather than the origin or destination of seawater transiting through that region, that is most immediately relevant to Earth’s heat budget. Here we use a recent gridded estimate of ocean heat transport to reveal the net effect on Earth’s heat budget, the ‘effective’ ocean heat transport, by removing internal ocean heat loops that have obscured the interpretation of measurements. The result demonstrates the overwhelming predominance of the tropical Pacific, which exports four times as much heat as is imported in the Atlantic and Arctic. It also highlights the unique ability of the Atlantic and Indian oceans to transport heat across the Equator—Northward and Southward, respectively. However, effective inter-ocean heat transports are smaller than expected, suggesting that global-scale seawater pathways play only a minor role in Earth’s heat budget. Effective heat transport in the global ocean is dominated by heat export from the tropical Pacific, whereas seawater transport pathways play only a minor role, according to an analysis of a gridded ocean data product.

Fidelity assessment of general circulation model simulated precipitation and temperature over Pakistan using a feature selection method

General Circulation Models (GCMs) provide vital information on the likely future climate, much needed for the effective planning and management of water resources. The performance assessment of GCMs has received significant attention in recent years for reliable estimation of future climate. Even though many approaches have been trialled in the ranking of GCMs for the selection of an appropriate ensemble, there is still a need to explore the potential of the state-of-the-art ranking approaches for a more dependable selection of GCMs suitable for a given task, over a region of interest. The present study assessed the potential of a state-of-the-art feature selection method known as Symmetrical Uncertainty (SU) in ranking 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs based on their ability to simulate monthly precipitation and the monthly average of daily maximum and minimum temperature for annual, monsoon and winter seasons. The performance of GCMs was assessed using gridded climate data obtained from Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU) and Princeton Global Meteorological Forcing dataset (Prin) over the period 1961–2005 considering Pakistan as the study area. The ranks obtained with SU were compared with those obtained using two well-established ranking approaches; (1) Compromise Programming (CP) and (2) Wavelet-based Skill Score (WSS). According to the results of this study, for the simulation of all the three climate variables in all seasons; CESM1-CAM5, HadGEM2-AO, NorESM1-M and HadGEM2-ES were identified as the best GCMs by SU, whereas CESM1-CAM5, HadGEM2-AO, NorESM1-M and GFDL-CM3 were identified as the best GCMs by CP, and CCSM4, CESM1-CAM5, GFDL-ESM2G and HadGEM2-ES by WSS. The comparison of ranks of GCMs obtained using the same ranking approach but based on different gridded data products showed more or less similar ranks for a given GCM. However, the differences were noticeable when the ranking was conducted with the same gridded data but employing different ranking approaches. The approach presented in this study can be extended to any number of GCMs and can be applied over any region, for the identification of the best performing ensemble of GCMs for a set of climate variables.

A probabilistic gridded product for daily precipitation extremes over the United States

Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new “probabilistic” gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj).

Automated retrieval, preprocessing, and visualization of gridded hydrometeorology data products for spatial-temporal exploratory analysis and intercomparison

Spatially-distributed time-series data support a range of environmental modeling and data research efforts. A critical first step to any such effort is acquiring interpolated hydrometeorological data. Standardized tools to facilitate this process into analyses have not been readily available for watershed scale research. Here, we introduce the Observatory for Gridded Hydrometeorology (OGH), an open source python library that fills this critical software gap by providing a cyberinfrastructure component to fetch and manage distributed data processed from regional and continental-scale gridded hydrometeorology products. Our approach involves annotating metadata to make gridded data products discoverable and usable within the software, enabling interoperability and reproducibility of models that use the data. This paper presents the design, architecture, and application of OGH using four commonly practiced use-cases with gridded time-series data at watershed scales. OGH and its associated annotations are distributed via Anaconda Cloud within conda-forge package repository. The tutorial Jupyter notebooks for each example use-case are available within the Freshwater Initiative Observatory repository (https://github.com/Freshwater-Initiative/Observatory). The examples are designed to utilize the compute resources and software libraries provided by HydroShare ((https://www.hydroshare.org/resource/87dc5742cf164126a11ff45c3307fd9d)).

Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan

The rough topography, harsh climate, and sparse monitoring stations have limited hydro-climatological studies in arid regions of Pakistan. Gauge-based gridded precipitation datasets provide an opportunity to assess the climate where stations are sparsely located. Though, the reliability of these datasets heavily depends on their ability to replicate the observed temporal variability and distribution patterns. Conventional correlation or error analyses are often not enough to justify the variability and distribution of precipitation. In the present study, mean bias error, mean absolute error, modified index of agreement, and Anderson–Darling test have been used to evaluate the performance of four widely used gauge-based gridded precipitation data products, namely, Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU); Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), Center for Climatic Research—University of Delaware (UDel) at stations located in semi-arid, arid, and hyper-arid regions in the Balochistan province of Pakistan. The result revealed that the performance of different products varies with climate. However, GPCC precipitation data was found to perform much better in all climatic regions in terms of most of the statistical assessments conducted. As the temporal variability and distribution of precipitation are very important in many hydrological and climatic applications, it can be expected that the methods used in this study can be useful for the better assessment of gauge-based data for various applications.

Bayesian inverse estimation of urban CO2 emissions: Results from a synthetic data simulation over Salt Lake City, UT

Top-down, data-driven models possess ample power to improve the accuracy of bottom-up carbon dioxide (CO2) emission inventories, and more work is needed to explore the merger of top-down and bottom-up estimates to better inform the metrics used to monitor global CO2 fluxes. Here we present a Bayesian inverse modeling framework over Salt Lake City, Utah, which utilizes available CO2 emission inventories to establish a synthetic data simulation aimed at exploring model uncertainties. Prescribing a high-resolution, urban-scale data product (Hestia) as the “true” emissions in the model, we combine prior emissions with an atmospheric transport model to derive modeled afternoon CO2 enhancements at six monitoring sites within the Salt Lake Valley during the month of September 2015. A global high-resolution gridded emissions data product (ODIAC) is used as the prior, and objective uncertainty structures are defined for both the a priori estimates and the transport model-data relationship which consider non-negligible spatial and temporal covariances. Optimized (posterior) emissions over the Salt Lake Valley agree closely with the assumed “true” emissions during afternoon times, while results including unconstrained times (e.g. night-time) lack such agreement. Both spatial and temporal correlations of prior errors were found to be necessary for obtaining a robust posterior estimate. Model sensitivity analyses are performed, which examine correlation length and time scales, model-data mismatch error, and measurement site network variability. Through these analyses, one measurement site is identified as being particularly prone to introducing bias into posterior emissions due to influences from a nearby point source. Increasing model-data mismatch error at this site is shown to reduce bias in the posterior without significantly compromising agreement with monthly averaged true emissions.

Changing station coverage impacts temperature trends in the Upper Colorado River basin

Over the Upper Colorado River basin (UCRB), temperatures in widely used gridded data products do not warm as much as mean temperatures from a stable set of U.S. Historical Climatology Network (USHCN) stations, located at generally lower elevations, in most months of the year. This is contrary to expectations of elevation‐dependent warming, which suggests that warming increases with elevation. These findings could reflect (a) a genuine absence of elevation‐dependent warming in the region, (b) systematic non‐climatic influences on either the USHCN stations or high‐elevation stations, including known inhomogeneities related to changes in the time of observation and instrumentation, or (c) suppression of an elevation‐dependent warming signal introduced by changes in the station network. While we cannot categorically dismiss the first two possibilities, we show here that over portions of the 20th century, gridded temperatures warm less than USHCN temperatures, and the difference cannot be explained by accounting for known inhomogeneities. These analyses suggest that changing station coverage in the UCRB has influenced trends in gridded temperature estimates that incorporate changing suites of stations over time. Specifically, increases in the number of high‐elevation stations in the UCRB may have led to an underestimation of elevation‐dependent warming, particularly during the spring. This phenomenon is unlikely limited to this specific basin and may be present in other high‐elevation watersheds across the western United States

Evaluating the Performance of Remotely Sensed Precipitation Estimates against In-Situ Observations during the September 2014 Mega-Flood in the Kashmir Valley

In the present study, 4 gridded satellite precipitation data products for September 2014 flood, IMERG (Integrated Multi-satellitE Retrievals for GPM), GSMaP (Global Satellite Mapping of Precipitation), TRMM-3B42 (Tropical Rainfall Measuring Mission) and INSAT-3D-IMR (INSAT Multispectral Rain), were evaluated against the Indian Meteorological Department rain-gauge data from Sep-1st to Sep-7th 2014. Three evaluation indices; Correlation coefficient (CC), the Relative bias (RB) and the Nash-Sutcliffe coefficient (NSC), were used to evaluate the robustness of satellite precipitation estimates with actual rainfall measurements. IMERG precipitation product has a near perfect positive CC and NSC values of 0.94 and 0.99 respectively; while the CC and NSC values are 0.7 and 0.5 for GSMaP_Gauge; 0.69 and 0.05 for INSAT-3D-IMR; and 0.9 and 0.8 for TRMM-3B42 respectively. The RB estimates indicate that IMERG, with a bias of 2%, is a best-fit dataset when compared to the surface rain-gauge observations. In contrast, TRMM-3B42, GSMaP and INSAT-3D-IMR have underestimation biases of −31%, −58%, and − 86% respectively. Analysis of the indices indicates that IMERG precipitation product performed better than other three satellite precipitation products owing to the closeness of values with surface gauge station data over Kashmir. Owing to scanty observation of rainfall in the region, IMERG has a potential to become a cost effective input data source for designing a flood early warning system (FEWS) for Kashmir. However, it is suggested to evaluate the robustness of different satellite-derived precipitation estimates compared to rain gauge observations by incorporating more extreme events from different mountain regions globally for establishing the best satellite derived precipitation product.

Looking beyond wildlife: using remote cameras to evaluate accuracy of gridded snow data

AbstractThe use of remote cameras is widespread in wildlife ecology, yet few examples exist of their utility for collecting environmental data. We used a novel camera trap method to evaluate the accuracy of gridded snow data in a mountainous region of the northeastern US. We were specifically interested in assessing (1) how snow depth observations from remote cameras compare with gridded climate data, (2) the sources of error associated with the gridded data and (3) the influence of spatial sampling on bias. We compared daily observations recorded by remote cameras with Snow Data Assimilation System (SNODAS) gridded predictions using data from three winters (2014–2016). Snow depth observations were correlated with SNODAS predictions for sites (R2 = 0.20) and regions (R2 = 0.16), yet we detected factors associated with SNODAS bias at both scales. Specifically, SNODAS underpredicted depths at high elevations, at sites with higher solar radiation, and within conifer‐dominated forest. Depths were most underpredicted at highest elevations, up to 44 and 26 cm on average at the site and region scales, respectively. Bias was greatest when predictions were lowest, occasionally predicting snow absence when depths were >100 cm at camera sites. We also detected breakdowns in accuracy when certain environmental conditions varied within the 1 km2 SNODAS grid cells. For example, underprediction was greatest when the solar radiation values of camera stations increased relative to the mean of the SNODAS grid cells. This relationship was most prominent in mountainous regions, suggesting that factors which influence solar radiation (e.g. topographic complexity) contribute to SNODAS inaccuracy. We caution using gridded snow data for ecological studies when bias is unknown. We suggest increased sampling to adjust for errors associated with gridded data products that arise from factors, such as forest cover and topographic variability. Increasing resolution and accuracy of climate data will improve predictions of species’ responses to climate change.

How accurate are the performances of gridded precipitation data products over Northeast China?

The assessment of changes in the precipitation over a region is an important task that is significant for climate modeling and for understanding and monitoring natural hazards and hydrological cycle characteristics. However, the accuracies of gridded precipitation data sets should be evaluated and examined in reference to ground information and observations, as these data can be affected by systematic errors. Therefore, the current study was carried out to evaluate the performances of long-term gridded data sets (from the Global Precipitation Climatology Centre (GPCC), Climate Research Unit (CRU) and University of Delaware (UDEL)) with station-based precipitation data obtained from the National Meteorological Administration (NMA) over Heilongjiang Province in Northeast China for the period from 1961 to 2005. For this purpose, this study adopted spatial comparisons among the data sets, the precipitation concentration index and drought frequency analysis based on the standardized precipitation index to evaluate the performances of these data sets in the entire study area. The results indicate that on spatial scales, the gridded data sets showed higher amounts of annual precipitation at most stations at the spatial scale than the NMA. The spatial correlations among the GPCC, UDEL and NMA were in good agreement compared with the CRU. The annual and seasonal precipitation concentration indexes suggested that the gridded data sets and station-based precipitation exhibited an irregular pattern, as 97% of the stations showed an extremely irregular pattern in the study area during the selected time period. The seasonal results of the precipitation concentration index showed that the gridded data products indicated that all of the stations exhibited irregular precipitation distributions (except during the summer), while the in situ station data indicated that most of the stations had a uniform pattern in the winter and summer. In the summer, the UDEL results were more similar to those of the NMA than those of the CRU and GPCC. The analysis based on gridded data sets revealed that 22–41% of the stations experienced a non-significant negative trend compared with the observed precipitation data sets. The drought frequency analysis based on in situ and gridded data was comparable. This study also revealed a new challenge regarding the utilization of gridded data sets due to discrepancies in the results. Therefore, it is very important to minimize these discrepancies by quantifying their uncertainties, identifying raw data systematic error structures, and applying satellite-based data to improve the quality of data in the region.

Agriculturally Relevant Climate Extremes and Their Trends in the World's Major Growing Regions

AbstractClimate extremes can negatively impact crop production, and climate change is expected to affect the frequency and severity of extremes. Using a combination of in situ station measurements (Global Historical Climatology Network's Daily data set) and multiple other gridded data products, a derived 1° data set of growing season climate indices and extremes is compiled over the major growing regions for maize, wheat, soybean, and rice for 1951–2006. This data set contains growing season climate indices that are agriculturally relevant, such as the number of hot days, duration of dry spells, and rainfall intensity. Before 1980, temperature‐related indices had few trends; after 1980, statistically significant warming trends exist for each crop in the majority of growing regions. In particular, crops have increasingly been exposed to extreme hot temperatures, above which yields have been shown to decline. Rainfall trends are less consistent compared to temperature, with some regions receiving more rainfall and others less. Anomalous temperature and precipitation conditions are shown to often occur concurrently, with dry growing seasons more likely to be hotter, have larger drought indices, and have larger vapor pressure deficits. This leads to the confluence of a variety of climate conditions that negatively impact crop yields. These results show a consistent increase in global agricultural exposure to negative climate conditions since 1980.

On the use of the GRACE normal equation of inter-satellite tracking data for estimation of soil moisture and groundwater in Australia

Abstract. An accurate estimation of soil moisture and groundwater is essential for monitoring the availability of water supply in domestic and agricultural sectors. In order to improve the water storage estimates, previous studies assimilated terrestrial water storage variation (ΔTWS) derived from the Gravity Recovery and Climate Experiment (GRACE) into land surface models (LSMs). However, the GRACE-derived ΔTWS was generally computed from the high-level products (e.g. time-variable gravity fields, i.e. level 2, and land grid from the level 3 product). The gridded data products are subjected to several drawbacks such as signal attenuation and/or distortion caused by a posteriori filters and a lack of error covariance information. The post-processing of GRACE data might lead to the undesired alteration of the signal and its statistical property. This study uses the GRACE least-squares normal equation data to exploit the GRACE information rigorously and negate these limitations. Our approach combines GRACE's least-squares normal equation (obtained from ITSG-Grace2016 product) with the results from the Community Atmosphere Biosphere Land Exchange (CABLE) model to improve soil moisture and groundwater estimates. This study demonstrates, for the first time, an importance of using the GRACE raw data. The GRACE-combined (GC) approach is developed for optimal least-squares combination and the approach is applied to estimate the soil moisture and groundwater over 10 Australian river basins. The results are validated against the satellite soil moisture observation and the in situ groundwater data. Comparing to CABLE, we demonstrate the GC approach delivers evident improvement of water storage estimates, consistently from all basins, yielding better agreement on seasonal and inter-annual timescales. Significant improvement is found in groundwater storage while marginal improvement is observed in surface soil moisture estimates.

Downscaling GRACE Remote Sensing Datasets to High-Resolution Groundwater Storage Change Maps of California’s Central Valley

NASA’s Gravity Recovery and Climate Experiment (GRACE) has already proven to be a powerful data source for regional groundwater assessments in many areas around the world. However, the applicability of GRACE data products to more localized studies and their utility to water management authorities have been constrained by their limited spatial resolution (~200,000 km2). Researchers have begun to address these shortcomings with data assimilation approaches that integrate GRACE-derived total water storage estimates into complex regional models, producing higher-resolution estimates of hydrologic variables (~2500 km2). Here we take those approaches one step further by developing an empirically based model capable of downscaling GRACE data to a high-resolution (~16 km2) dataset of groundwater storage changes over a portion of California’s Central Valley. The model utilizes an artificial neural network to generate a series of high-resolution maps of groundwater storage change from 2002 to 2010 using GRACE estimates of variations in total water storage and a series of widely available hydrologic variables (PRISM precipitation and temperature data, digital elevation model (DEM)-derived slope, and Natural Resources Conservation Service (NRCS) soil type). The neural network downscaling model is able to accurately reproduce local groundwater behavior, with acceptable Nash-Sutcliffe efficiency (NSE) values for calibration and validation (ranging from 0.2445 to 0.9577 and 0.0391 to 0.7511, respectively). Ultimately, the model generates maps of local groundwater storage change at a 100-fold higher resolution than GRACE gridded data products without the use of computationally intensive physical models. The model’s simulated maps have the potential for application to local groundwater management initiatives in the region.