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Renato Prata de Moraes Frasson
,
Michael J. Turmon
,
Michael T. Durand
, and
Cédric H. David

Abstract

The Surface Water and Ocean Topography (SWOT) mission will allow the estimation of discharge in rivers wider than 100 m, filling important gaps in the network of in situ measurements. This novel source of discharge observations has the potential to enable significant progress toward closing Earth’s water budget and creating new understanding of the water cycle. Quantifying the uncertainty in the SWOT estimates of discharge, mapping the error sources, and understanding their relative importance is essential to the fulfillment of this potential. Here, we break the SWOT discharge production process into its essential parts: 1) retrieval of river width and water surface heights and slopes, 2) estimation of unobservable parameters, and 3) computation of discharge with the selected flow law, and through a Monte Carlo simulation study, we assess the sensitivity of the overall discharge error to each of these parts. We analyze the discharge error characteristics in terms of bias, error standard deviation, and the correlation between true and retrieved discharges and map the contribution of the essential discharge production elements to each of the error metrics. Our study revealed that biases in parameters are the most important source of discharge biases, yet we found that a larger than expected fraction of the discharge biases can be attributed to observation errors. Surprisingly, we found that parameter biases are also the most important contributor to discharge error standard deviation and to the deterioration of the correlation between truth and retrieved discharges, which were previously thought of as being controlled by observation errors.

Free access
Shusen Wang
,
Junhua Li
, and
Hazen A. J. Russell

Abstract

Developing effective methods for estimating regional-scale surface water storage change (ΔSW) has become increasingly important for water resources studies and environmental impact assessment. Three methods for estimating monthly ΔSW are proposed in this study, of which one is based on land surface runoff and two that use water body water budgets. Water areas observed by Landsat satellites for Canada’s entire landmass are used for evaluation of the results. The surface runoff method achieved the least satisfactory results, with large errors in the cold season or dry regions. The two water-budget methods demonstrated significant improvements, particularly when water area dynamics is considered in the estimation of the water body water budget. The three methods performed consistently across different climate regions in the country and showed better correlations with observations over wet climate regions than over dry regions with poorly connected hydrological system. The results also showed the impact of glacier and permanent snow melts over the Rocky Mountains on basin-scale surface water dynamics. The methods and outputs from this study can be used for calibrating and validating hydrological and climate models, assessing climate change and human disturbance impacts on regional water resources, and filling the ΔSW data gaps in GRACE-based total water storage decompositions studies.

Significance Statement

The purpose of this study is to develop and evaluate methods for estimating regional-scale surface water storage change. This is important because information on surface water dynamics is limited for water resources studies and environmental impact assessment. Our study makes available two new methods which significantly improve on surface water storage estimation from the traditional runoff model. A guide on controls of surface water dynamics is provided for regions under various hydroclimate and physiographic–hydraulic conditions and reveals the influence of glacier melt on surface water variations.

Open access
Divya Upadhyay
,
Sudhanshu Dixit
, and
Udit Bhatia

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.

Free access
Tao Tang
,
Xuhui Lee
,
Keer Zhang
,
Lei Cai
,
David M. Lawrence
, and
Elena Shevliakova

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.

Free access
Yalei You
,
George Huffman
,
Veljko Petkovic
,
Lisa Milani
,
John X. Yang
,
Ardeshir Ebtehaj
,
Sajad Vahedizade
, and
Guojun Gu

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.

Free access
Wolfgang Hanft
,
Jian Zhang
, and
Micheal Simpson

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.

Free access
Amirkhamza Murodov
,
Lan Cuo
,
Ning Li
,
Davlatkhudzha Murodov
,
Mei Hou
, and
Gulfam Hussain

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.

Free access
Kai Yang
and
Chenghai Wang

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.

Open access
Peng Ji
,
Xing Yuan
,
Chunxiang Shi
,
Lipeng Jiang
,
Guoqing Wang
, and
Kun Yang

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.

Free access
Benjamin Bass
,
Stefan Rahimi
,
Naomi Goldenson
,
Alex Hall
,
Jesse Norris
, and
Zachary J. Lebow

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.

Free access