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Development of a High-Resolution Gridded Daily Meteorological Dataset over Sub-Saharan Africa: Spatial Analysis of Trends in Climate Extremes
No full textAssessing changes in the frequency and intensity of extreme meteorological events and their impact on water resources, agriculture, and infrastructure is necessary to adequately prepare and adapt to future change. This is a challenge in data-sparse regions such as sub-Saharan Africa, where a lack of high-density and temporally consistent long-term in situ measurements complicates the analysis. To address this, a temporally homogenous and high-temporal- and high-spatial-resolution meteorological dataset is developed over sub-Saharan Africa (5°S-25°N), covering the time period between 1979 and 2005. It is developed by spatially downscaling the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis to a 0.1° spatial resolution, detecting and correcting for temporal inhomogeneities, and by removing random errors and biases by assimilating quality-controlled and gap-filled Global Summary of the Day (GSOD) in situ measurements. The dataset is then used to determine the statistical significance and magnitude of changes in climate extremes between 1979 and 2005. The results suggest a shift in the distribution of daily maximum and minimum temperatures toward a warmer mean with a faster increase in warm than cold events. Changes in the mean annual precipitation and heavy rainfall events are significant only in regions affected by the Sahel droughts of the 1970s and 1980s.</p
Spatial downscaling of precipitation using adaptable random forests
No full textThis paper introduces Prec-DWARF (Precipitation Downscaling With Adaptable Random Forests), a novel machine-learning based method for statistical downscaling of precipitation. Prec-DWARF sets up a nonlinear relationship between precipitation at fine resolution and covariates at coarse/fine resolution, based on the advanced binary tree method known as Random Forests (RF). In addition to a single RF, we also consider a more advanced implementation based on two independent RFs which yield better results for extreme precipitation. Hourly gauge-radar precipitation data at 0.125° from NLDAS-2 are used to conduct synthetic experiments with different spatial resolutions (0.25°, 0.5°, and 1°). Quantitative evaluation of these experiments demonstrates that Prec-DWARF consistently outperforms the baseline (i.e., bilinear interpolation in this case) and can reasonably reproduce the spatial and temporal patterns, occurrence and distribution of observed precipitation fields. However, Prec-DWARF with a single RF significantly underestimates precipitation extremes and often cannot correctly recover the fine-scale spatial structure, especially for the 1° experiments. Prec-DWARF with a double RF exhibits improvement in the simulation of extreme precipitation as well as its spatial and temporal structures, but variogram analyses show that the spatial and temporal variability of the downscaled fields are still strongly underestimated. Covariate importance analysis shows that the most important predictors for the downscaling are the coarse-scale precipitation values over adjacent grid cells as well as the distance to the closest dry grid cell (i.e., the dry drift). The encouraging results demonstrate the potential of Prec-DWARF and machine-learning based techniques in general for the statistical downscaling of precipitation
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Probabilistic Seasonal Forecasting of African Drought by Dynamical Models
No full textAs a natural phenomenon, drought can have devastating impacts on local populations through food insecurity and famine in the developing world, such as in Africa. In this study, the authors have established a seasonal hydrologic forecasting system for Africa. The system is based on the Climate Forecast System, version 2 (CFSv2), and the Variable Infiltration Capacity (VIC) land surface model. With a set of 26-yr (1982-2007) seasonal hydrologic hindcasts run at 0.258, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soilmoisture percentile as indices. In terms ofBrier skill score (BSS), the system is more skillful than climatology out to 3-5 months, except for the forecast of soil moisture drought over centralAfrica. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soilmoisture than the SPI6. Drought forecasts based on SPI6 are generallymore skillful than for soilmoisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soilmoisture drought forecast can bemore skillful than SPI6 at the beginning of the rainy season over western and southern Africa because of the strong annual cycle. Singular value decomposition (SVD) analysis of African precipitation and global SSTs indicates that CFSv2 reproduces the ENSO dominance on rainy season drought forecasts quite well, but the corresponding SVD mode fromobservations and CFSv2 only account for less than 24% and 31% of the covariance, respectively, suggesting that further understanding of drought drivers, including regional atmospheric dynamics and land-atmosphere coupling, is necessary.</p
Using Satellite Land Surface Temperature to Parameterize Sub-grid Tiling Schemes and Enable Tile-level Calibration of Land Surface Model
No full textTo better simulate terrestrial energy and water processes, great efforts have been made to improve the representation of spatial heterogeneity in land surface models. HydroBlocks, a field-scale resolving land surface model, was developed to address this challenge while minimizing increases in computational demands by employs a new tiling framework – the hierarchical multivariate clustering (HMC) approach. The HMC is used to cluster grid cells with similar surface characteristics into hydrologic response units (HRUs, i.e., sub-grid tiles) using high-resolution data. HRUs model land surface processes separately and HRUs are connected through subsurface flow. Hence, the HRU configuration plays a critical role in the model performance. Furthermore, since the outputs for each HRU can be mapped in space, it is possible to explicitly calibrate the model at the tile level based on satellite-derived data. To investigate the advantages that satellite observations can have to tune HydroBlocks, the model was set up to run from 2013 to 2019 at the 30-meter spatial resolution and hourly temporal resolution over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), U.S. This study demonstrated that HydroBlocks benefits from considering the temporal mean and standard deviation map of Landsat LST when generating HRUs. The effects of inputting the albedo, emissivity, and leaf area index derived directly from MODIS into the model were also explored. Finally, six soil and vegetation parameters in HydroBlocks were calibrated by maximizing the linear correlation coefficient of the time-series of simulated LST and observed MODIS LST at each HRU. The regional-level and site-level evaluation indicate the effectiveness of the calibration approach.</p
Evaluation of multi-model simulated soil moisture in NLDAS-2
No full textThe North American Land Data Assimilation System (NLDAS) phase 2 (NLDAS-2) has generated 31-years (1979-2008) of water and energy products from four state-of-the-art land surface models (Noah, Mosaic, SAC, VIC). The soil moisture data from these models have been used for operational drought monitoring activities, but so far have not yet been comprehensively evaluated. In this study, three available in situ soil moisture observation data sets in the United States were used to evaluate the model-simulated soil moisture for different time scales varying from daily to annual. First, we used the observed multiple layer monthly and annual mean soil moisture from the Illinois Climate Network to evaluate 20-years (January 1985-December 2004) of model-simulated soil moisture in terms of skill and analysis of error statistics. Second, we utilized 6-years (1 January 1997-31 December 2002) of daily soil moisture observed from 72 sites over the Oklahoma Mesonet network to assess daily and monthly simulation skill and errors for 3 model soil layers (0-10. cm, 10-40. cm, 40-100. cm). Third, we extended the daily assessment to sites over the continental United States using 8-years (1 January 2002-31 December 2009) of observations for 121 sites from the Soil Climate Analysis Network (SCAN). Overall, all models are able to capture wet and dry events and show high skill (in most cases, anomaly correlation is larger than 0.7), but display large biases when compared to in situ observations. These errors may come from model errors (i.e., model structure error, model parameter error), forcing data errors, and in situ soil moisture measurement errors. For example, all models simulate less soil moisture due to lack of modeled irrigation and ground water processes in Illinois, Oklahoma, and the other Midwest states.</p
Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates
No full textAccurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Recent satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). The framework was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m soil moisture. We validated the downscaled soil moisture estimates at four experimental watersheds with dense in-situ soil moisture networks in the United States and obtained overall high correlations (> 0.81) and good mean KGE score (0.56). The downscaled product captures the spatial and temporal soil moisture dynamics better than SMAP L3 and L4 product alone at both field and watershed scales. Our results highlight the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications
Climate change impact on water resources in a basin in West Virginia
No full textThis paper investigates climate change impact on the water resources in the Greenbrier basin using a distributed hydrological model VIC and future climate series. The GCM outputs under the SRES A2 greenhouse gas emission scenario is downscaled and bias-corrected by the BCCAQ method to obtain the future climate series. The VIC model performance is satisfactory with the Nash–Sutcliffe efficiency coefficient (NSE) of 0.62 and 0.58 in calibration and validation periods. The bias-corrected precipitation and temperature indicate a warmer and more humid climate with precipitation and temperature increase by 14% and 1.8°C in the future. Under climate change background, the mean annual cycles of water balance components keep similar seasonal fluctuation but have larger magnitudes in the future. The discharge in the future also has close monthly distribution with that in the historical observations. The results show that the future discharge is larger than historical observation, implying water resources would be more abundant in summer from 2046 to 2065. The hydrological simulations in the Greenbrier basin have a system error of underestimating the peak flows, and the extreme discharge would be larger and more frequent in the mid of 21st century.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Changing water availability during the African maize-growing season, 1979–2010
Full text linkUnderstanding how global change is impacting African agriculture requires a full physical accounting of water supply and demand, but accurate, gridded data on key drivers (e.g., humidity) are generally unavailable. We used a new bias-corrected meteorological dataset to analyze changes in precipitation (supply), potential evapotranspiration (Ep, demand), and water availability (expressed as the ratio P/Ep) in 20 countries (focusing on their maize-growing regions and seasons), between 1979 and 2010, and the factors driving changes in Ep. Maizegrowing areas in Southern Africa, particularly South Africa, benefitted from increased water availability due in large part to demand declines driven primarily by declining net radiation, increasing vapor pressure, and falling temperatures (with no effect from changing windspeed), with smaller increases in supply. Sahelian zone countries in West Africa, as well as Ethiopia in East Africa, had strong increases in availability driven primarily by rainfall rebounding from the long-term Sahelian droughts, with little change or small reductions in demand. However, intraseasonal supply variability generally increased in West and East Africa. Across all three regions, declining net radiation contributed downwards pressure on demand, generally over-riding upwards pressure caused by increasing temperatures, the regional effects of which were largest in East Africa. A small number of countries, mostly in or near East Africa (Tanzania and Malawi) experienced declines in water availability primarily due to decreased rainfall, but exacerbated by increasing demand. Much of the reduced water availability in East Africa occurred during the more sensitive middle part of the maize-growing season, suggesting negative consequences for maize production. © 2014 IOP Publishing Ltd
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