Browse
Abstract
Quantifying uncertainties in estimating future hydropower production directly or indirectly affects India’s energy security, planning, and management. The chaotic and nonlinear nature of atmospheric processes results in considerable internal climate variability (ICV) for future projections of climate variables. Multiple initial condition ensembles (MICE) and multimodel ensembles (MME) are often used to analyze the role of ICV and model uncertainty in precipitation and temperature. However, there are limited studies focusing on quantifying the role of internal variability on impact variables, including hydropower production. In this study, we analyze the role of ICV and model uncertainty on three prominent hydropower plants in India using MICE of EC-Earth3 and MME from CMIP6. We estimate the streamflow projections for all ensemble members using the Variable Infiltration Capacity hydrological model. We estimate maximum hydropower production generated using monthly release and hydraulic head available at the reservoir. We also analyzed the role of bias correction in hydropower production. The results show that ICV plays a significant role in estimating streamflow and hydropower potential for monsoon and throughout the year, respectively. Model uncertainty contributes more to total uncertainty than ICV in estimating the streamflow and potential hydropower. However, ICV is increasing toward the far term (2075–2100). We also show that bias correction does not preserve ICV in estimating the streamflow. Although there is an increase in uncertainty for estimated streamflow, mean hydropower shows a decrease toward the far term for February–May, more prominent for MICE than MME. The results suggest a need to incorporate uncertainty due to internal variability for addressing power security in changing climate scenarios.
Abstract
Quantifying uncertainties in estimating future hydropower production directly or indirectly affects India’s energy security, planning, and management. The chaotic and nonlinear nature of atmospheric processes results in considerable internal climate variability (ICV) for future projections of climate variables. Multiple initial condition ensembles (MICE) and multimodel ensembles (MME) are often used to analyze the role of ICV and model uncertainty in precipitation and temperature. However, there are limited studies focusing on quantifying the role of internal variability on impact variables, including hydropower production. In this study, we analyze the role of ICV and model uncertainty on three prominent hydropower plants in India using MICE of EC-Earth3 and MME from CMIP6. We estimate the streamflow projections for all ensemble members using the Variable Infiltration Capacity hydrological model. We estimate maximum hydropower production generated using monthly release and hydraulic head available at the reservoir. We also analyzed the role of bias correction in hydropower production. The results show that ICV plays a significant role in estimating streamflow and hydropower potential for monsoon and throughout the year, respectively. Model uncertainty contributes more to total uncertainty than ICV in estimating the streamflow and potential hydropower. However, ICV is increasing toward the far term (2075–2100). We also show that bias correction does not preserve ICV in estimating the streamflow. Although there is an increase in uncertainty for estimated streamflow, mean hydropower shows a decrease toward the far term for February–May, more prominent for MICE than MME. The results suggest a need to incorporate uncertainty due to internal variability for addressing power security in changing climate scenarios.
Abstract
In this study, we investigate the air temperature response to land-use and land-cover change (LULCC; cropland expansion and deforestation) using subgrid land model output generated by a set of CMIP6 model simulations. Our study is motivated by the fact that ongoing land-use activities are occurring at local scales, typically significantly smaller than the resolvable scale of a grid cell in Earth system models. It aims to explore the potential for a multimodel approach to better characterize LULCC local climatic effects. On an annual scale, the CMIP6 models are in general agreement that croplands are warmer than primary and secondary land (psl; mainly forests, grasslands, and bare ground) in the tropics and cooler in the mid–high latitudes, except for one model. The transition from warming to cooling occurs at approximately 40°N. Although the surface heating potential, which combines albedo and latent heat flux effects, can explain reasonably well the zonal mean latitudinal subgrid temperature variations between crop and psl tiles in the historical simulations, it does not provide a good prediction on subgrid temperature for other land tile configurations (crop vs forest; grass vs forest) under Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) forcing scenarios. A subset of simulations with the CESM2 model reveals that latitudinal subgrid temperature variation is positively related to variation in net surface shortwave radiation and negatively related to variation in the surface energy redistribution factor, with a dominant role from the latter south of 30°N. We suggest that this emergent relationship can be used to benchmark the performance of land surface parameterizations and for prediction of local temperature response to LULCC.
Abstract
In this study, we investigate the air temperature response to land-use and land-cover change (LULCC; cropland expansion and deforestation) using subgrid land model output generated by a set of CMIP6 model simulations. Our study is motivated by the fact that ongoing land-use activities are occurring at local scales, typically significantly smaller than the resolvable scale of a grid cell in Earth system models. It aims to explore the potential for a multimodel approach to better characterize LULCC local climatic effects. On an annual scale, the CMIP6 models are in general agreement that croplands are warmer than primary and secondary land (psl; mainly forests, grasslands, and bare ground) in the tropics and cooler in the mid–high latitudes, except for one model. The transition from warming to cooling occurs at approximately 40°N. Although the surface heating potential, which combines albedo and latent heat flux effects, can explain reasonably well the zonal mean latitudinal subgrid temperature variations between crop and psl tiles in the historical simulations, it does not provide a good prediction on subgrid temperature for other land tile configurations (crop vs forest; grass vs forest) under Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) forcing scenarios. A subset of simulations with the CESM2 model reveals that latitudinal subgrid temperature variation is positively related to variation in net surface shortwave radiation and negatively related to variation in the surface energy redistribution factor, with a dominant role from the latter south of 30°N. We suggest that this emergent relationship can be used to benchmark the performance of land surface parameterizations and for prediction of local temperature response to LULCC.
Abstract
This study assesses the level-2 snowfall retrieval results from 11 passive microwave radiometers generated by the version 5 Goddard profiling algorithm (GPROF) relative to two spaceborne radars: CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Measurement (GPM) Ku-band Precipitation Radar (KuPR). These 11 radiometers include six conical scanning radiometers [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), its successor sensor AMSR2, GPM Microwave Imager (GMI), and three Special Sensor Microwave Imager/Sounders (SSMIS)] and five cross-track scanning radiometers [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHS)]. Results show that over ocean conical scanning radiometers have better detection and intensity estimation skills than cross-track sensors, likely due to the availability and usage of the low-frequency channels (e.g., 19 and 37 GHz). Over land, AMSR-E and AMSR2 have noticeably worse performance than other sensors, primarily due to the lack of higher than 89-GHz channels (e.g., 150, 166, and 183 GHz). Over both land and ocean, all 11 sensors severely underestimate the snowfall intensity, which propagates to the widely used level 3 precipitation product [i.e., Integrated Multi-satelliteE Retrievals for GPM (IMERG)]. These conclusions hold regardless of using either KuPR or CPR as the reference, though the statistical metrics vary quantitatively. The conclusions drawn from these comparisons apply solely to the GPROF version 5 algorithm.
Abstract
This study assesses the level-2 snowfall retrieval results from 11 passive microwave radiometers generated by the version 5 Goddard profiling algorithm (GPROF) relative to two spaceborne radars: CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Measurement (GPM) Ku-band Precipitation Radar (KuPR). These 11 radiometers include six conical scanning radiometers [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), its successor sensor AMSR2, GPM Microwave Imager (GMI), and three Special Sensor Microwave Imager/Sounders (SSMIS)] and five cross-track scanning radiometers [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHS)]. Results show that over ocean conical scanning radiometers have better detection and intensity estimation skills than cross-track sensors, likely due to the availability and usage of the low-frequency channels (e.g., 19 and 37 GHz). Over land, AMSR-E and AMSR2 have noticeably worse performance than other sensors, primarily due to the lack of higher than 89-GHz channels (e.g., 150, 166, and 183 GHz). Over both land and ocean, all 11 sensors severely underestimate the snowfall intensity, which propagates to the widely used level 3 precipitation product [i.e., Integrated Multi-satelliteE Retrievals for GPM (IMERG)]. These conclusions hold regardless of using either KuPR or CPR as the reference, though the statistical metrics vary quantitatively. The conclusions drawn from these comparisons apply solely to the GPROF version 5 algorithm.
Abstract
The radar bright band is caused by melting ice crystals, and results in inflated reflectivity observations. If uncorrected, the bright band can result in large errors in radar-derived quantitative precipitation estimation (QPE). In the operational Multi-Radar Multi-Sensor (MRMS) system up to version 12.1, the effects of the bright band are corrected through the use of a reflectivity-only, tilt-based apparent vertical profile of reflectivity (tilt-VPR). This study utilizes dual-polarization (dual-pol) radar observations to improve the tilt-VPR methodology. To accomplish this, a brightband area delineation was developed within the MRMS framework and the brightband top and bottom heights were identified for individual tilts of radar data. This information was used to develop a radially dependent dual-pol VPR (dpVPR) model that can better correct reflectivity in situations of nonisotropic bright bands and low brightband events. This algorithm has been tested on 14 varying brightband events across the CONUS and compared with the tilt-VPR and the National Weather Service Weather Surveillance Radar-1988 Doppler Level-3 Digital Precipitation Rate (DPR) products. The radially dependent dpVPR correction provided a more accurate detection of brightband areas and a more effective reduction in QPE errors within and above the bright band than the tilt-VPR and DPR QPEs, especially for precipitation events with low melting layers or with strong variability of vertical motions. The brightband delineation and dpVPR methodology are also evaluated in the real-time MRMS testbed for their robustness and computational efficiency and has been transitioned into operations in 2022.
Abstract
The radar bright band is caused by melting ice crystals, and results in inflated reflectivity observations. If uncorrected, the bright band can result in large errors in radar-derived quantitative precipitation estimation (QPE). In the operational Multi-Radar Multi-Sensor (MRMS) system up to version 12.1, the effects of the bright band are corrected through the use of a reflectivity-only, tilt-based apparent vertical profile of reflectivity (tilt-VPR). This study utilizes dual-polarization (dual-pol) radar observations to improve the tilt-VPR methodology. To accomplish this, a brightband area delineation was developed within the MRMS framework and the brightband top and bottom heights were identified for individual tilts of radar data. This information was used to develop a radially dependent dual-pol VPR (dpVPR) model that can better correct reflectivity in situations of nonisotropic bright bands and low brightband events. This algorithm has been tested on 14 varying brightband events across the CONUS and compared with the tilt-VPR and the National Weather Service Weather Surveillance Radar-1988 Doppler Level-3 Digital Precipitation Rate (DPR) products. The radially dependent dpVPR correction provided a more accurate detection of brightband areas and a more effective reduction in QPE errors within and above the bright band than the tilt-VPR and DPR QPEs, especially for precipitation events with low melting layers or with strong variability of vertical motions. The brightband delineation and dpVPR methodology are also evaluated in the real-time MRMS testbed for their robustness and computational efficiency and has been transitioned into operations in 2022.
Abstract
The Amu Darya contributed 70% of the flow to the Aral Sea in central Asia before the 1960s, when the Amu Darya streamflow to the Aral Sea started to dwindle. The severe environmental and socioeconomic disaster happened mainly due to intensified water abstraction with the backdrop of climate change. However, knowledge of up to the most recent extreme climate conditions and their changes, as well as their relations to streamflow in the basin, is still lacking. This study aims to understand extreme hydrometeorological conditions and their changes, as well as their relations in the past several decades, especially in the upper Amu Darya basin. The spatial patterns of the means of all extreme temperature indices followed the elevation gradient. The majority of the basin showed an increasing trend in extreme warm events but a decreasing trend in extreme cold events. The north of the upper basin had over 1000 mm annual precipitation, and the east had less than 300 mm annual precipitation. Overall, the upper Amu Darya basin underwent a wetting and warming annual trend. Annual streamflow in the upper subbasins was less than 750 m3 s−1, but together they produced over 1500 m3 s−1 flow in the middle reach and basin outlet. Streamflow change varied among subbasins. Correlations between climatic factors and streamflow at annual time steps were weak but distinct at monthly time steps with lagged effects. In highland subbasins with high coverage of glaciers and snow, temperature minima and maxima impacts were opposite and overwhelmed precipitation, whereas in lowland subbasins, precipitation was more important.
Abstract
The Amu Darya contributed 70% of the flow to the Aral Sea in central Asia before the 1960s, when the Amu Darya streamflow to the Aral Sea started to dwindle. The severe environmental and socioeconomic disaster happened mainly due to intensified water abstraction with the backdrop of climate change. However, knowledge of up to the most recent extreme climate conditions and their changes, as well as their relations to streamflow in the basin, is still lacking. This study aims to understand extreme hydrometeorological conditions and their changes, as well as their relations in the past several decades, especially in the upper Amu Darya basin. The spatial patterns of the means of all extreme temperature indices followed the elevation gradient. The majority of the basin showed an increasing trend in extreme warm events but a decreasing trend in extreme cold events. The north of the upper basin had over 1000 mm annual precipitation, and the east had less than 300 mm annual precipitation. Overall, the upper Amu Darya basin underwent a wetting and warming annual trend. Annual streamflow in the upper subbasins was less than 750 m3 s−1, but together they produced over 1500 m3 s−1 flow in the middle reach and basin outlet. Streamflow change varied among subbasins. Correlations between climatic factors and streamflow at annual time steps were weak but distinct at monthly time steps with lagged effects. In highland subbasins with high coverage of glaciers and snow, temperature minima and maxima impacts were opposite and overwhelmed precipitation, whereas in lowland subbasins, precipitation was more important.
Abstract
Frozen soil distributed over alpine cold regions causes obvious changes in the soil hydrothermal regime and influences the water–heat exchanges between land and atmosphere. In this study, by comparing the effects of snow cover anomalies and frozen soil thawing anomalies on the soil hydrothermal regime, the impact of the frozen soil thawing anomalies in spring on precipitation in early summer over the Tibetan Plateau (TP) was investigated via diagnostic analysis and model simulations. The results show that a delay (advance) in the anomalies of frozen soil thawing in spring can induce distinct cold (warm) anomalies in the soil temperature in the eastern TP. These soil temperature cold (warm) anomalies further weaken (enhance) the surface diabatic heating over the mideastern TP; meanwhile, the anomalies in the western TP are inconspicuous. Compared to the albedo effect of snow cover anomalies, impacts of frozen soil thawing anomalies on soil hydrothermal regime and surface diabatic heating can persist longer from April to June. Corresponding to the anomalous delay (advance) of frozen soil thawing, the monsoon cell is weakened (enhanced) over the southern and northern TP, resulting in less (more) water vapor advection over the eastern TP and more (less) water vapor advection over the southwestern TP. This difference in water vapor advection induces a west–east reversed pattern of precipitation anomalies in June over the TP. The results have potential for improving our understanding of the interactions between the cryosphere and climate in cold regions.
Significance Statement
Frozen soil and snow are widely distributed over alpine and high-latitude cold regions, and their feedbacks to climate have attracted much attention. The purpose of this study is to investigate the role of frozen soil in effects of snow cover anomalies on surface diabatic heating and its feedback to subsequent precipitation over the Tibetan Plateau. The results highlight that frozen soil modulates the effect of snow cover anomalies on the soil hydrothermal regime from April to June and interseasonal variations of frozen soil thawing anomaly zones result in a thermal contrast between the western and eastern Tibetan Plateau, which further lead to a reversed pattern of early summer precipitation anomalies over the Tibetan Plateau. These findings emphasize the role of frozen soil in land–atmosphere interactions.
Abstract
Frozen soil distributed over alpine cold regions causes obvious changes in the soil hydrothermal regime and influences the water–heat exchanges between land and atmosphere. In this study, by comparing the effects of snow cover anomalies and frozen soil thawing anomalies on the soil hydrothermal regime, the impact of the frozen soil thawing anomalies in spring on precipitation in early summer over the Tibetan Plateau (TP) was investigated via diagnostic analysis and model simulations. The results show that a delay (advance) in the anomalies of frozen soil thawing in spring can induce distinct cold (warm) anomalies in the soil temperature in the eastern TP. These soil temperature cold (warm) anomalies further weaken (enhance) the surface diabatic heating over the mideastern TP; meanwhile, the anomalies in the western TP are inconspicuous. Compared to the albedo effect of snow cover anomalies, impacts of frozen soil thawing anomalies on soil hydrothermal regime and surface diabatic heating can persist longer from April to June. Corresponding to the anomalous delay (advance) of frozen soil thawing, the monsoon cell is weakened (enhanced) over the southern and northern TP, resulting in less (more) water vapor advection over the eastern TP and more (less) water vapor advection over the southwestern TP. This difference in water vapor advection induces a west–east reversed pattern of precipitation anomalies in June over the TP. The results have potential for improving our understanding of the interactions between the cryosphere and climate in cold regions.
Significance Statement
Frozen soil and snow are widely distributed over alpine and high-latitude cold regions, and their feedbacks to climate have attracted much attention. The purpose of this study is to investigate the role of frozen soil in effects of snow cover anomalies on surface diabatic heating and its feedback to subsequent precipitation over the Tibetan Plateau. The results highlight that frozen soil modulates the effect of snow cover anomalies on the soil hydrothermal regime from April to June and interseasonal variations of frozen soil thawing anomaly zones result in a thermal contrast between the western and eastern Tibetan Plateau, which further lead to a reversed pattern of early summer precipitation anomalies over the Tibetan Plateau. These findings emphasize the role of frozen soil in land–atmosphere interactions.
Abstract
With the improvement of meteorological forcings and surface parameters, high-resolution land surface modeling is expected to provide locally relevant information. Yet, its added value over the state-of-the-art global reanalysis products requires long-term evaluations over large areas, given uneven climate warming and significant land cover change. Here, the Conjunctive Surface–Subsurface Process version 2 (CSSPv2) model, with a reasonable representation of runoff generation, subgrid soil moisture variability and urban dynamics, is calibrated and used to perform a 6-km resolution simulation over China during 1979–2017. Evaluations against observations at thousands of stations and several satellite-based products show that the CSSPv2 has 67%, 29%, and 15% lower simulation errors for snow depth, evapotranspiration (ET), and surface and root-zone soil moisture, respectively, than nine global products. The median Kling–Gupta efficiency of the streamflow for 83 river basins is 0.66 after bulk calibrations, which is 0.38 higher than that of global datasets. The CSSPv2 also accurately simulates urban heat islands (UHIs) and the patterns and magnitudes of long-term snow depth, ET, and soil moisture trends. However, the global products do not detect UHIs and overestimate the trends (or show opposite trends) of snow depth and ET. Sensitivity experiments with coarse-resolution forcings and surface parameters reveal that advanced model physics and high-resolution surface parameters are vital for improved simulations of snow depth, ET, soil moisture, and UHIs, whereas high-resolution meteorological forcings are critical for modeling long-term trends. Our research emphasizes the substantial added value of long-term high-resolution land surface modeling to present global products at continental scales.
Significance Statement
Highly heterogeneous changes of terrestrial water and energy require kilometer-scale land surface information for the adaptation. High-resolution land surface modeling has been regarded as a promising approach to provide locally relevant information, but most applications are limited to a small region or a short period. By performing sets of 6-km resolution simulations over China during 1979–2017 with the Conjunctive Surface–Subsurface Process version 2 land model, here we show that high-resolution modeling has 15%–67% lower simulation errors of snow depth, streamflow, evapotranspiration, and soil moisture than nine global products, and the improvement is mainly attributed to the advances in model physical parameterizations and high-resolution surface parameters. Our results emphasize the great added value of kilometer-scale land surface modeling at continental scales.
Abstract
With the improvement of meteorological forcings and surface parameters, high-resolution land surface modeling is expected to provide locally relevant information. Yet, its added value over the state-of-the-art global reanalysis products requires long-term evaluations over large areas, given uneven climate warming and significant land cover change. Here, the Conjunctive Surface–Subsurface Process version 2 (CSSPv2) model, with a reasonable representation of runoff generation, subgrid soil moisture variability and urban dynamics, is calibrated and used to perform a 6-km resolution simulation over China during 1979–2017. Evaluations against observations at thousands of stations and several satellite-based products show that the CSSPv2 has 67%, 29%, and 15% lower simulation errors for snow depth, evapotranspiration (ET), and surface and root-zone soil moisture, respectively, than nine global products. The median Kling–Gupta efficiency of the streamflow for 83 river basins is 0.66 after bulk calibrations, which is 0.38 higher than that of global datasets. The CSSPv2 also accurately simulates urban heat islands (UHIs) and the patterns and magnitudes of long-term snow depth, ET, and soil moisture trends. However, the global products do not detect UHIs and overestimate the trends (or show opposite trends) of snow depth and ET. Sensitivity experiments with coarse-resolution forcings and surface parameters reveal that advanced model physics and high-resolution surface parameters are vital for improved simulations of snow depth, ET, soil moisture, and UHIs, whereas high-resolution meteorological forcings are critical for modeling long-term trends. Our research emphasizes the substantial added value of long-term high-resolution land surface modeling to present global products at continental scales.
Significance Statement
Highly heterogeneous changes of terrestrial water and energy require kilometer-scale land surface information for the adaptation. High-resolution land surface modeling has been regarded as a promising approach to provide locally relevant information, but most applications are limited to a small region or a short period. By performing sets of 6-km resolution simulations over China during 1979–2017 with the Conjunctive Surface–Subsurface Process version 2 land model, here we show that high-resolution modeling has 15%–67% lower simulation errors of snow depth, streamflow, evapotranspiration, and soil moisture than nine global products, and the improvement is mainly attributed to the advances in model physical parameterizations and high-resolution surface parameters. Our results emphasize the great added value of kilometer-scale land surface modeling at continental scales.
Abstract
In this study, we calibrate a regional climate model’s (RCM) underlying land surface model (LSM). In addition to providing a realistic representation of runoff across the hydroclimatically diverse western United States, this is done to take advantage of the RCM’s ability to physically resolve meteorological forcing data in ungauged regions, and to prepare the calibrated hydrologic model for tight coupling, or the ability to represent land surface–atmosphere interactions, with the RCM. Specifically, we use a 9-km resolution meteorological forcing dataset across the western United States, from the fifth generation ECMWF Reanalysis (ERA5) downscaled by the Weather Research Forecasting (WRF) regional climate model, as an offline forcing for Noah-Multiparameterization (Noah-MP). We detail the steps involved in producing an LSM capable of accurately representing runoff, including physical parameterization selection, parameter calibration, and regionalization to ungauged basins. Based on our model evaluation from 1954 to 2021 for 586 basins with daily natural streamflow, the streamflow bias is reduced from 24.2% to 4.4%, and the median daily Nash–Sutcliffe efficiency (NSE) is improved from 0.12 to 0.36. When validating against basins with monthly natural streamflow data, we obtain a similar reduction in bias and a median monthly NSE improvement from 0.18 to 0.56. In this study, we also discover the optimal setup when using a donor-basin method to regionalize parameters to ungauged basins, which can vary by 0.06 NSE for unique designs of this regionalization method.
Significance Statement
This study provides useful guidance for improving a land surface model to accurately represent runoff across a spatially extensive and hydroclimatically diverse region (the western United States). The land surface model is updated to represent runoff more accurately at gauged basins, and then additionally updated for basins without observational data using a mathematical approach called the donor-basin method. We make use of a regional climate model’s reanalysis-derived meteorological data and its underlying land surface model to achieve realistic runoff. The calibrated land surface model can thus be tightly coupled in subsequent studies in a manner that should more accurately reflect runoff conditions. Findings from this study will serve as a useful reference for the atmospheric (and hydrologic) modeling communities and their ability to represent large-scale hydrology accurately.
Abstract
In this study, we calibrate a regional climate model’s (RCM) underlying land surface model (LSM). In addition to providing a realistic representation of runoff across the hydroclimatically diverse western United States, this is done to take advantage of the RCM’s ability to physically resolve meteorological forcing data in ungauged regions, and to prepare the calibrated hydrologic model for tight coupling, or the ability to represent land surface–atmosphere interactions, with the RCM. Specifically, we use a 9-km resolution meteorological forcing dataset across the western United States, from the fifth generation ECMWF Reanalysis (ERA5) downscaled by the Weather Research Forecasting (WRF) regional climate model, as an offline forcing for Noah-Multiparameterization (Noah-MP). We detail the steps involved in producing an LSM capable of accurately representing runoff, including physical parameterization selection, parameter calibration, and regionalization to ungauged basins. Based on our model evaluation from 1954 to 2021 for 586 basins with daily natural streamflow, the streamflow bias is reduced from 24.2% to 4.4%, and the median daily Nash–Sutcliffe efficiency (NSE) is improved from 0.12 to 0.36. When validating against basins with monthly natural streamflow data, we obtain a similar reduction in bias and a median monthly NSE improvement from 0.18 to 0.56. In this study, we also discover the optimal setup when using a donor-basin method to regionalize parameters to ungauged basins, which can vary by 0.06 NSE for unique designs of this regionalization method.
Significance Statement
This study provides useful guidance for improving a land surface model to accurately represent runoff across a spatially extensive and hydroclimatically diverse region (the western United States). The land surface model is updated to represent runoff more accurately at gauged basins, and then additionally updated for basins without observational data using a mathematical approach called the donor-basin method. We make use of a regional climate model’s reanalysis-derived meteorological data and its underlying land surface model to achieve realistic runoff. The calibrated land surface model can thus be tightly coupled in subsequent studies in a manner that should more accurately reflect runoff conditions. Findings from this study will serve as a useful reference for the atmospheric (and hydrologic) modeling communities and their ability to represent large-scale hydrology accurately.
Abstract
We analyze uncertainty in model-based estimates of probable maximum precipitation (PMP) as used in dam spillway design. Our focus is on model-based PMP derived from Weather Research and Forecasting (WRF) Model reconstructions of severe historical storms, amplified by the addition of moisture in the boundary conditions [so-called relative humidity maximization (RHM)]. By scaling moisture and predicting the resulting precipitation, the model-based approach arguably is more realistic than currently used techniques [documented in NOAA’s Hydrometeorological Reports (HMRs)], which assume that precipitation scales linearly with moisture. Despite the important improvement this represents, model-based PMP is subject to several sources of uncertainty that have slowed adoption in operational settings. We analyze an ensemble of PMP simulations that reflect recognized sources of uncertainty including the following: 1) initial condition error, 2) choice of physics parameterizations, and 3) upscale propagating model errors. We apply this ensemble approach to the Feather River watershed (Oroville Dam) in California for the storms of February 1986 and January 1997, which produced some of the largest floods on record at that location, after carrying out in-depth evaluations of model reconstructions. Differences in the maximized 72-h precipitation totals across the 56 ensemble members we produced for each storm are modest, ranging from ±7% of ensemble mean. Our results suggest that while model-based PMP estimates should be interpreted as a range of values, model uncertainty appears to be relatively small for the major atmospheric river–driven flood events we investigated.
Abstract
We analyze uncertainty in model-based estimates of probable maximum precipitation (PMP) as used in dam spillway design. Our focus is on model-based PMP derived from Weather Research and Forecasting (WRF) Model reconstructions of severe historical storms, amplified by the addition of moisture in the boundary conditions [so-called relative humidity maximization (RHM)]. By scaling moisture and predicting the resulting precipitation, the model-based approach arguably is more realistic than currently used techniques [documented in NOAA’s Hydrometeorological Reports (HMRs)], which assume that precipitation scales linearly with moisture. Despite the important improvement this represents, model-based PMP is subject to several sources of uncertainty that have slowed adoption in operational settings. We analyze an ensemble of PMP simulations that reflect recognized sources of uncertainty including the following: 1) initial condition error, 2) choice of physics parameterizations, and 3) upscale propagating model errors. We apply this ensemble approach to the Feather River watershed (Oroville Dam) in California for the storms of February 1986 and January 1997, which produced some of the largest floods on record at that location, after carrying out in-depth evaluations of model reconstructions. Differences in the maximized 72-h precipitation totals across the 56 ensemble members we produced for each storm are modest, ranging from ±7% of ensemble mean. Our results suggest that while model-based PMP estimates should be interpreted as a range of values, model uncertainty appears to be relatively small for the major atmospheric river–driven flood events we investigated.
Abstract
Anthropogenic climate change is affecting rivers worldwide, threatening water availability and altering the risk of natural hazards. Understanding the pattern of regional streamflow trends can help to inform region-specific policies to mitigate and adapt to any negative impacts on society and the environment. We present a benchmark dataset of long, near-natural streamflow records across Aotearoa New Zealand (NZ) and the first nationwide analysis of observed spatiotemporal streamflow trends. Individual records rarely have significant trends, but when aggregated within homogenous hydrologic regions (determined through cluster analyses), significant regional trends emerge. A multitemporal approach that uses all available data for each region and considers trend significance over time reveals the influence of decadal variability in some seasons and regions, and consistent trends in others. Over the last 50+ years, winter streamflow has significantly increased in the west South Island and has significantly decreased in the north North Island; summer streamflow has significantly decreased for most of the North Island; autumn streamflow has generally dried nationwide; and spring streamflow has increased along the west coast and decreased along the east coast. Correlations between streamflow and dynamic and thermodynamic climate indices reveal the dominant drivers of hydrologic behavior across NZ. Consistencies between the observed near-natural streamflow trends and observed changes in circulation and thermodynamic processes suggest possible climate change impacts on NZ hydrology.
Abstract
Anthropogenic climate change is affecting rivers worldwide, threatening water availability and altering the risk of natural hazards. Understanding the pattern of regional streamflow trends can help to inform region-specific policies to mitigate and adapt to any negative impacts on society and the environment. We present a benchmark dataset of long, near-natural streamflow records across Aotearoa New Zealand (NZ) and the first nationwide analysis of observed spatiotemporal streamflow trends. Individual records rarely have significant trends, but when aggregated within homogenous hydrologic regions (determined through cluster analyses), significant regional trends emerge. A multitemporal approach that uses all available data for each region and considers trend significance over time reveals the influence of decadal variability in some seasons and regions, and consistent trends in others. Over the last 50+ years, winter streamflow has significantly increased in the west South Island and has significantly decreased in the north North Island; summer streamflow has significantly decreased for most of the North Island; autumn streamflow has generally dried nationwide; and spring streamflow has increased along the west coast and decreased along the east coast. Correlations between streamflow and dynamic and thermodynamic climate indices reveal the dominant drivers of hydrologic behavior across NZ. Consistencies between the observed near-natural streamflow trends and observed changes in circulation and thermodynamic processes suggest possible climate change impacts on NZ hydrology.
