Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood-risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the development of data-driven artificial neural network (ANN) models of streamflow. Four well-known formal data splitting methods are applied to 754 catchments from Australia and the U.S. to develop 902,483 ANN models. Results clearly show that the choice of the method used for data allocation has a significant impact on model performance, particularly for runoff data that are more highly skewed, highlighting the importance of considering the impact of data splitting when developing hydrological models. The statistical behavior of the data splitting methods investigated is discussed and guidance is offered on the selection of the most appropriate data splitting methods to achieve representative evaluation performance for streamflow data with different statistical properties. Although our results are obtained for data-driven models, they highlight the fact that this issue is likely to have a significant impact on all types of hydrological models, especially conceptual rainfall-runoff models.
Roy, Tirthankar; Gupta, Hoshin V.; Serrat-Capdevila, Aleix; Valdes, Juan B. (COPERNICUS GESELLSCHAFT MBH, 2017-02-14)
Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making diagnostic structural improvements to hydrologic models whenever possible.
Snow initialization is crucial for weather and seasonal prediction, but the National Centers for Environmental Prediction (NCEP) operational models have been found to produce too little snow water equivalent, partly because they assume a constant and unrealistically low snow density for the snowpack. One possible solution is to use the snow density formulation from the Noah land model used in NCEP operational forecast models. While this solution is better than the constant density assumption, the seasonal evolution of snow density in Noah is still found to be unrealistic, through the evaluation of both the offline Noah model output and the Noah snow density formulation itself. A physically based snow density parameterization is then developed, which performs considerably better than the Noah parameterization based on the measurements from the SNOTEL network over the western United States and Alaska. It also performs better than the snow density schemes used in three other models. This parameterization could be easily implemented in NCEP operational snow initialization. With the consideration of up to 10 snow layers, this parameterization can also be applied to multilayer snowpack initiation or to estimate snow water equivalent from in situ and airborne snow depth measurements.
Atmospheric reanalyses have been used in many studies to investigate the variabilities and trends of precipitation because of their global coverage and long record; however, their results must be properly analyzed and their uncertainties must be understood. In this study, precipitation estimates from five global reanalyses [ERA-Interim; MERRA, version 2 (MERRA2); JRA-55; CFSR; and 20CR, version 2c (20CRv2c)] and one regional reanalysis (NARR) are compared against the CPC Unified Gauge-Based Analysis (CPCUGA) and GPCP over the contiguous United States (CONUS) during the period 1980-2013. Reanalyses capture the variability of the precipitation distribution over the CONUS as observed in CPCUGA and GPCP, but large regional and seasonal differences exist. Compared with CPCUGA, global reanalyses generally overestimate the precipitation over the western part of the country throughout the year and over the northeastern CONUS during the fall and winter seasons. These issues may be associated with the difficulties models have in accurately simulating precipitation over complex terrain and during snowfall events. Furthermore, systematic errors found in five global reanalyses suggest that their physical processes in modeling precipitation need to be improved. Even though negative biases exist in NARR, its spatial variability is similar to both CPCUGA and GPCP; this is anticipated because it assimilates observed precipitation, unlike the global reanalyses. Based on CPCUGA, there is an average decreasing trend of -1.38mm yr(-1) over the CONUS, which varies depending on the region with only the north-central to northeastern parts of the country having positive trends. Although all reanalyses exhibit similar interannual variation as observed in CPCUGA, their estimated precipitation trends, both linear and spatial trends, are distinct from CPCUGA.
Behrangi, Ali; Gardner, Alex; Reager, John T.; Fisher, Joshua B.; Yang, Daqing; Huffman, George J.; Adler, Robert F. (AMER METEOROLOGICAL SOC, 2018-11)
Ten years of terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) were used to estimate high-latitude snowfall accumulation using a mass balance approach. The estimates were used to assess two common gauge-undercatch correction factors (CFs): the Legates climatology (CF-L) utilized in the Global Precipitation Climatology Project (GPCP) and the Fuchs dynamic correction model (CF-F) used in the Global Precipitation Climatology Centre (GPCC) monitoring product. The two CFs can be different by more than 50%. CF-L tended to exceed CF-F over northern Asia and Eurasia, while the opposite was observed over North America. Estimates of snowfall from GPCP, GPCC-L (GPCC corrected by CF-L), and GPCC-F (GPCC corrected by CF-F) were 62%, 64%, and 46% more than GPCC over northern Asia and Eurasia. The GRACE-based estimate (49% more than GPCC) was the closest to GPCC-F. We found that as near-surface air temperature decreased, the products increasingly underestimated the GRACE-based snowfall accumulation. Overall, GRACE showed that CFs are effective in improving GPCC estimates. Furthermore, our case studies and overall statistics suggest that CF-F is likely more effective than CF-L in most of the high-latitude regions studied here. GPCP showed generally better skill than GPCC-L, which might be related to the use of satellite data or additional quality controls on gauge inputs to GPCP. This study suggests that GPCP can be improved if it employs CF-L instead of CF-F to correct for gauge undercatch. However, this implementation requires further studies, region-specific analysis, and operational considerations.
This paper presents an aerosol characterization study from 2003 to 2015 for the Mexico City Metropolitan Area using remotely sensed aerosol data, ground-based measurements, air mass trajectory modeling, aerosol chemical composition modeling, and reanalysis data for the broader Megalopolis of Central Mexico region. The most extensive biomass burning emissions occur between March and May concurrent with the highest aerosol optical depth, ultraviolet aerosol index, and surface particulate matter (PM) mass concentration values. A notable enhancement in coarse PM levels is observed during vehicular rush hour periods on weekdays versus weekends owing to nonengine-related emissions such as resuspended dust. Among wet deposition species measured, PM2.5, PM10, and PMcoarse (PM10-PM2.5) were best correlated with NH4+, SO42-, and Ca2+, suggesting that the latter three constituents are important components of the aerosol seeding raindrops that eventually deposit to the surface in the study region. Reductions in surface PM mass concentrations were observed in 2014-2015 owing to reduced regional biomass burning as compared to 2003-2013.
Jiang, Zhe; Worden, John R.; Worden, Helen; Deeter, Merritt; Jones, Dylan B. A.; Arellano, Avelino F.; Henze, Daven K. (COPERNICUS GESELLSCHAFT MBH, 2017-04-06)
Long-term measurements from satellites and surface stations have demonstrated a decreasing trend of tropospheric carbon monoxide (CO) in the Northern Hemisphere over the past decade. Likely explanations for this decrease include changes in anthropogenic, fires, and/or biogenic emissions or changes in the primary chemical sink hydroxyl radical (OH). Using remotely sensed CO measurements from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument, in situ methyl chloroform (MCF) measurements from the World Data Centre for Greenhouse Gases (WDCGG) and the adjoint of the GEOS-Chem model, we estimate the change in global CO emissions from 2001 to 2015. We show that the loss rate of MCF varied by 0.2 % in the past 15 years, indicating that changes in global OH distributions do not explain the recent decrease in CO. Our two-step inversion approach for estimating CO emissions is intended to mitigate the effect of bias errors in the MOPITT data as well as model errors in transport and chemistry, which are the primary factors contributing to the uncertainties when quantifying CO emissions using these remotely sensed data. Our results confirm that the decreasing trend of tropospheric CO in the Northern Hemisphere is due to decreasing CO emissions from anthropogenic and biomass burning sources. In particular, we find decreasing CO emissions from the United States and China in the past 15 years, and unchanged anthropogenic CO emissions from Europe since 2008. We find decreasing trends of biomass burning CO emissions from boreal North America, boreal Asia and South America, but little change over Africa. In contrast to prior results, we find that a positive trend in CO emissions is likely for India and southeast Asia.
We present joint analyses of satellite-observed combustion products to examine bulk characteristics of combustion in megacities and fire regions. We use retrievals of CO, NO2 and CO2 from NASA/Terra Measurement of Pollution In The Troposphere, NASA/Aura Ozone Monitoring Instrument, and JAXA Greenhouse Gases Observing Satellite to estimate atmospheric enhancements of these co-emitted species based on their spatiotemporal variability (spread, sigma) within 14 regions dominated by combustion emissions. We find that patterns in sigma(XCO)/sigma(XCO2) and sigma(XCO)/sigma(XNO2) are able to distinguish between combustion types across the globe. These patterns show distinct groupings for biomass burning and the developing/developed status of a region that are not well represented in global emissions inventories. We show here that such multi-species analyses can provide constraints on emission inventories, and be useful in monitoring trends and understanding regional-scale combustion.
Shingler, Taylor; Sorooshian, Armin; Ortega, Amber; Crosbie, E.; Wonaschütz, Anna; Perring, Anne E.; Beyersdorf, Andreas; Ziemba, L. D.; Jimenez, J. L.; Campuzano-Jost, P.; et al. (AMER GEOPHYSICAL UNION, 2016-11-27)
This study reports a detailed set of ambient observations of optical/physical shrinking of particles from exposure to water vapor with consistency across different instruments and regions. Data have been utilized from (i) a shipboard humidified tandem differential mobility analyzer during the Eastern Pacific Emitted Aerosol Cloud Experiment in 2011, (ii) multiple instruments on the NASA DC-8 research aircraft during the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys in 2013, and (iii) the Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe during ambient measurements in Tucson, Arizona, during summer 2014 and winter 2015. Hygroscopic growth factor (ratio of humidified-to-dry diameter, GF = D-p,D-wet/D-p,D-dry) and f(RH) (ratio of humidified-to-dry scattering coefficients) values below 1 were observed across the range of relative humidity (RH) investigated (75-95%). A commonality of observations of GF and f(RH) below 1 in these experiments was the presence of particles enriched with carbonaceous matter, especially from biomass burning. Evidence of externally mixed aerosol, and thus multiple GFs with at least one GF < 1, was observed concurrently with f(RH) < 1 during smoke periods. Possible mechanisms responsible for observed shrinkage are discussed and include particle restructuring, volatilization effects, and refractive index modifications due to aqueous processing resulting in optical size modification. To further investigate ambient observations of GFs and f(RH) values less than 1, it is recommended to add an optional prehumidification bypass module to hygroscopicity instruments, to preemptively collapse particles prior to controlled RH measurements.
Ralph, F. Martin; Galarneau, Thomas J. (AMER METEOROLOGICAL SOC, 2017-09)
Between North America's Sierra Madre and Rocky Mountains exists a little-recognized terrain "gap.'' This study defines the gap, introduces the term "Chiricahua Gap,'' and documents the role of easterly transport of water vapor through the gap in modulating summer monsoon precipitation in southeastern Arizona. The gap is near the Arizona-New Mexico border north of Mexico and is approximately 250 km wide by 1 km deep. It is the lowest section along a 3000-km length of the Continental Divide from 168 to 45 degrees N and represents 80% of the total cross-sectional area below 2.5 km MSL open to horizontal water vapor transport in that region. This study uses reanalyses and unique upper-air observations in a case study and a 15-yr climatology to show that 72% (76%) of the top-quartile (decile) monsoon precipitation days in southeast Arizona during 2002-16 occurred in conditions with easterly water vapor transport through the Chiricahua Gap on the previous day.
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