Using a Gridded Global Dataset to Characterize Regional Hydroclimate in Central Chile

Demaria, E. M.; Maurer, Edwin P.; Sheffield, Justin; Bustos, E.; Poblete, David Figueroa; Vicuña, Sebastián; Meza, Francisco

doi:10.1175/jhm-d-12-047.1

Cited by 22 publications

(11 citation statements)

References 38 publications

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“…Table 2). The correspondence of all products at a daily timescale and in all the validation areas was found to be comparably weak, and the findings are in agreement with earlier studies (Cohen Liechti et al, 2012;Dembélé and Zwart, 2016).…”

Section: Discussionsupporting

confidence: 91%

“…The agreement of all the rainfall products increases from daily to dekadal and monthly timescales (Fig. 8), and this is consistent with other studies (Cohen Liechti et al, 2012;Dembélé and Zwart, 2016;Kimani et al, 2017). Generally, CHIRPS, with a high spatial resolution, followed by CHIRP and ARC2, was the best performing rainfall product in terms of correlation, biases, and errors and in characterizing regional rainfall characteristics.…”

Section: Discussionsupporting

confidence: 90%

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Evaluation of multiple climate data sources for managing environmental resources in East Africa

Gebrechorkos

Hülsmann

Bernhofer

2018

Hydrol. Earth Syst. Sci.

143

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Abstract. Managing environmental resources under conditions of climate change and extreme climate events remains among the most challenging research tasks in the field of sustainable development. A particular challenge in many regions such as East Africa is often the lack of sufficiently long-term and spatially representative observed climate data. To overcome this data challenge we used a combination of accessible data sources based on station data, earth observations by remote sensing, and regional climate models. The accuracy of the Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation (CHIRP), CHIRP with Station data (CHIRPS), Observational-Reanalysis Hybrid (ORH), and regional climate models (RCMs) are evaluated against station data obtained from the respective national weather services and international databases. We did so by performing a comparison in three ways: point to pixel, point to area grid cell average, and stations' average to area grid cell average over 21 regions of East Africa: 17 in Ethiopia, 2 in Kenya, and 2 in Tanzania. We found that the latter method provides better correlation and significantly reduces biases and errors. The correlations were analysed at daily, dekadal (10 days), and monthly resolution for rainfall and maximum and minimum temperature (Tmax and Tmin) covering the period of 1983–2005. At a daily timescale, CHIRPS, followed by ARC2 and CHIRP, is the best performing rainfall product compared to ORH, individual RCMs (I-RCM), and RCMs' mean (RCMs). CHIRPS captures the daily rainfall characteristics well, such as average daily rainfall, amount of wet periods, and total rainfall. Compared to CHIRPS, ARC2 showed higher underestimation of the total (−30 %) and daily (−14 %) rainfall. CHIRP, on the other hand, showed higher underestimation of the average daily rainfall (−53 %) and duration of dry periods (−29 %). Overall, the evaluation revealed that in terms of multiple statistical measures used on daily, dekadal, and monthly timescales, CHIRPS, CHIRP, and ARC2 are the best performing rainfall products, while ORH, I-RCM, and RCMs are the worst performing products. For Tmax and Tmin, ORH was identified as the most suitable product compared to I-RCM and RCMs. Our results indicate that CHIRPS (rainfall) and ORH (Tmax and Tmin), with higher spatial resolution, should be the preferential data sources to be used for climate change and hydrological studies in areas of East Africa where station data are not accessible.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 90%

Evaluation of multiple climate data sources for managing environmental resources in East Africa

Gebrechorkos

Hülsmann

Bernhofer

2018

Hydrol. Earth Syst. Sci.

143

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“…We used precipitation, temperature, and diurnal temperature range (DTR), which are available at three hourly, daily, and monthly time steps from 1948 to present at a horizontal resolution of 1°latitude/longitude. The data set has been similarly used by others in studies of regional climate outside of CA (e.g., Bohn et al [2007], Demaria et al [2012], Sheffield et al [2010], and Wang et al [2011]). In addition, we use the University of East Anglia Climate Research Unit gridded monthly time series of precipitation and temperature fields [Mitchell and Jones, 2005], and Hadley Center sea surface temperature (SST) data set (1870-2012) [Hurrell et al, 2008], in order to compare the model simulations during the historical period in the CA.…”

Section: Study Area and Historical/observed Data Setmentioning

confidence: 99%

Evaluation of historical and future simulations of precipitation and temperature in central Africa from CMIP5 climate models

et al. 2016

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Global and regional climate change assessments rely heavily on the general circulation model (GCM) outputs such as provided by the Coupled Model Intercomparison Project phase 5 (CMIP5). Here we evaluate the ability of 25 CMIP5 GCMs to simulate historical precipitation and temperature over central Africa and assess their future projections in the context of historical performance and intermodel and future emission scenario uncertainties. We then apply a statistical bias correction technique to the monthly climate fields and develop monthly downscaled fields for the period of 1948–2099. The bias‐corrected and downscaled data set is constructed by combining a suite of global observation and reanalysis‐based data sets, with the monthly GCM outputs for the 20th century, and 21st century projections for the medium mitigation (representative concentration pathway (RCP)45) and high emission (RCP85) scenarios. Overall, the CMIP5 models simulate temperature better than precipitation, but substantial spatial heterogeneity exists. Many models show limited skill in simulating the seasonality, spatial patterns, and magnitude of precipitation. Temperature projections by the end of the 21st century (2070–2099) show a robust warming between 2 and 4°C across models, whereas precipitation projections vary across models in the sign and magnitude of change (−9% to 27%). Projected increase in precipitation for a subset of models (single model ensemble (SME)) identified based on performance metrics and causal mechanisms are slightly higher compared to the full multimodel ensemble (MME) mean; however, temperature projections are similar between the two ensemble means. For the near‐term (2021–2050), neither the historical performance nor choice of models is related to the precipitation projections, indicating that natural variability dominated any signal. With fewer models, the “blind” MME approach will have larger uncertainties in future precipitation projections compared to projections by the SME models. We propose the latter a better approach in regions that lack quality climate observations. Our analyses also show that the choice of model and emission scenario dominate the uncertainty in precipitation projections, whereas the emission scenario dominates the temperature projections. Although our analyses are done for central Africa, the final Bias‐Corrected Spatially Downscaled data set is available for global land areas. The framework for climate change assessment and the data will be useful for a variety of climate assessment, impact, and adaptation studies.

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“…The accuracy of the simulated soil processes in land surface models is strongly tied to a robust characterization of the underlying soil properties within these models; in other words, the model parameters (e.g., water retention curve parameters) can play a key role in the modeled processes (Demaria et al, 2012;Hou et al, 2012;Melsen et al, 2016;Rosero et al, 2010). For example, under heavy rainfall the partitioning of precipitation into infiltration and surface runoff can vary strongly depending on the antecedent soil moisture conditions and soil composition; under identical conditions, a soil with high clay and low sand content will commonly have a much higher fraction of surface runoff than a soil with high sand and low The significant uncertainty in existing soil property maps is one of the primary reasons for the continued reliance on parameter calibration in hydrologic and land surface models.…”

Section: Introductionmentioning

confidence: 99%

POLARIS Soil Properties: 30‐m Probabilistic Maps of Soil Properties Over the Contiguous United States

Chaney

Minasny

Herman

et al. 2019

Water Resources Research

109

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Soils play a critical role in the cycling of water, energy, and carbon in the Earth system. Until recently, due primarily to a lack of soil property maps of a sufficiently high‐quality and spatial detail, a minor emphasis has been placed on providing high‐resolution measured soil parameter estimates for land surface models and hydrologic models. This study introduces Probabilistic Remapping of SSURGO (POLARIS) soil properties—a database of 30‐m probabilistic soil property maps over the contiguous United States (CONUS). The mapped variables over CONUS include soil texture, organic matter, pH, saturated hydraulic conductivity, Brooks‐Corey and Van Genuchten water retention curve parameters, bulk density, and saturated water content. POLARIS soil properties was assembled by (1) depth harmonizing and aggregating the pedons in the National Cooperative Soil Survey Soil Characterization Database and the components in Soil Survey Geographic Database into a database of 21,481 different soil series, each soil series having its own vertical profiles of different soil properties, (2) pruning the original POLARIS soil series maps using conventional soil maps to improve soil series prediction accuracy, and (3) merging the assembled soil series databases with the pruned POLARIS soil series maps to construct the soil property maps over CONUS. POLARIS soil properties includes 100‐bin histograms for each layer and variable per grid cell and a series of summary statistics at 30‐, 300‐, and 3,000‐m spatial resolution. Evaluation of POLARIS soil properties using in situ measurements shows an average R2 of 0.41, normalized root‐mean‐square error of 12%, and a normalized mean absolute error of 8.8%.

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Using a Gridded Global Dataset to Characterize Regional Hydroclimate in Central Chile

Cited by 22 publications

References 38 publications

Evaluation of multiple climate data sources for managing environmental resources in East Africa

Evaluation of multiple climate data sources for managing environmental resources in East Africa

Evaluation of historical and future simulations of precipitation and temperature in central Africa from CMIP5 climate models

POLARIS Soil Properties: 30‐m Probabilistic Maps of Soil Properties Over the Contiguous United States

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