Browse

You are looking at 1 - 10 of 2,614 items for :

  • Journal of Hydrometeorology x
  • Refine by Access: All Content x
Clear All
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.

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

Abstract

In this study, we calibrate a regional climate model’s (RCMs) 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 RCMs 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 9km resolution meteorological forcing dataset across the western United States (US), from ECMWF Reanalysis 5th Generation (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.

Restricted access
Siqi Yang
,
Jiangyuan Zeng
,
Wenjie Fan
, and
Yaokui Cui

Abstract

Root-zone soil moisture (RZSM) is an important variable in land–atmosphere interactions, notably affecting the global climate system. Contrary to satellite-based acquisition of surface soil moisture, RZSM is generally obtained from model-based simulations. In this study, in situ observations from the Naqu and Pali networks that represent different climatic conditions over the Tibetan Plateau (TP) and a triple collocation (TC) method are used to evaluate model-based RZSM products, including Global Land Evaporation Amsterdam Model (GLEAM) (versions 3.5a and 3.5b), Global Land Data Assimilation System (GLDAS) (versions 2.1 and 2.2), and the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5). The evaluation results based on in situ observations indicate that all products tend to overestimate but could generally capture the temporal variation, and ERA5 exhibits the best performance with the highest R (0.875) and the lowest unbiased RMSE (ubRMSE; 0.015 m3 m−3) against in situ observations in the Naqu network. In the TC analysis, similar results are obtained: ERA5 has the best performance with the highest TC-derived R (0.785) over the entire TP, followed by GLEAM v3.5a (0.746) and GLDAS-2.1 (0.682). Meanwhile, GLEAM v3.5a and GLDAS-2.1 outperform GLEAM v3.5b and GLDAS-2.2 over the entire TP, respectively. Besides, possible error causes in evaluating these RZSM products are summarized, and the effectiveness of TC method is also evaluated with two dense networks, finding that TC method is reliable since TC-derived R is close to ground-derived R, with only 6.85% mean relative differences. These results using both in situ observations and TC method may provide a new perspective for the soil moisture product developers to further enhance the accuracy of model-based RZSM over the TP.

Significance Statement

The purpose of this study is to better understand the quality and applicability of GLEAM, GLDAS, and ERA5 RZSM products over the TP using both in situ observations and the triple collocation (TC) method, making it better applied to climate and hydrological research. This study provides four standard statistical metrics evaluation based on in situ observations, as well as the reliable metric, that is, correlation coefficient (R) derived from TC method, and highlights that TC-based evaluation could supplement the ground-based validation, especially over the data-scarce TP region.

Restricted access
Laura E Queen
,
Sam Dean
,
Dáithí Stone
,
Roddy Henderson
, and
James Renwick

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 data set of long, near-natural streamflow records across Aotearoa New Zealand (NZ) and the first nation-wide 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 multi-temporal approach which 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 nation-wide; 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.

Restricted access
John R. Christy

Abstract

Time series of snowfall observations from over 500 stations in Oregon (OR) and Washington (WA) were generated for subregions of these states. Data problems encountered were as follows: 1) monthly totals in printed reports prior to 1940 that were not in the digital archive, 2) archived data listed as “missing” that were available, 3) digitized reports after 2010 eliminated good data, and 4) “zero” totals incorrectly entered in the official archive rather than “missing,” especially after 1980. Though addressing these was done, there is reduced confidence that some regional time series are representative of true long-term trends, especially for regions with few systematically reporting stations. For most regions characterized by consistent monitoring and with the most robust statistical reproducibility, we find no statistically significant trends in their periods of record (up to 131 years) for November–April seasonal totals through April 2020. This result includes the main snowfall regions of the Cascade Range. However, snowfall in some lower-elevation areas of OR and WA appear to have experienced declining trends, consistent with an increase in northeastern Pacific Ocean temperatures. Finally, previously constructed time series through April 2011 for regions in California are updated through April 2020 to include the recent, exceptionally low seasonal totals on the western slopes of the Sierra Nevada. This update indicates 2014/15 was the record lowest, 2013/14 was the 5th lowest, and 2012/13 was the 14th lowest of 142 years. Even so, the 1879–2020 linear trend in this key watershed region, though −2.6% decade−1, was not significantly different from zero due to high interannual variability and reconstruction uncertainty.

Restricted access
Hongxing Zheng
,
Francis H.S. Chiew
, and
Lu Zhang

Abstract

Dominant hydrological processes of a catchment could shift due to a changing climate. This climate-induced hydrological nonstationarity could affect the reliability of future runoff projection developed using a hydrological model calibrated for the historical period as the model or parameters may no longer be suitable under a different future hydroclimate. This paper explores whether competing parameterization approaches proposed to account for hydrological nonstationarity could improve the robustness of future runoff projection compared to the traditional approach where the model is calibrated targeting overall model performance over the entire historical period. The modeling experiments are carried out using climate and streamflow datasets from southeastern Australia, which has experienced a long drought and exhibited noticeable hydrological nonstationarity. The results show that robust multicriteria calibration based on the Pareto front can provide a more consistent model performance over contrasting hydroclimate conditions, but at a slight expense of increased bias over the entire historical period compared to the traditional approach. However, the robust calibration does not necessarily result in a more reliable projection of future runoff. This is because the systematic bias in any parameterization approach would propagate from the historical period to the future period and would largely be cancelled out when estimating the relative runoff change. Ensemble simulations combining results from different parameterization considerations could produce a more inclusive range of future runoff projection as it covers the uncertainties due to model parameterization.

Open access
Xiong Zhou
,
Guohe Huang
,
Yurui Fan
,
Xiuquan Wang
, and
Yongping Li

Abstract

Long-term hydrological projections can vary substantially depending on the combination of meteorological forcing dataset, hydrologic model (HM), emissions scenario, and natural climate variability. Identifying dominant sources of model spread in an ensemble of hydrologic projections is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management; however, it is not well understood due to the multifactor and multiscale complexities involved in the long-term hydrological projections. Therefore, a stepwise clustered Bayesian (SCB) ensemble method will be first developed to improve the performance of long-term hydrological projections. Meanwhile, a mixed-level factorial inference (MLFI) approach is employed to estimate multiple uncertainties in hydrological projections over the Jing River basin (JRB). MLFI is able to reveal the main and interactive effects of the anthropogenic emission and model choices on the SCB ensemble projections. The results suggest that the daily maximum temperature under RCP8.5 in the 2050s and 2080s is expected to respectively increase by 3.2° and 5.2°C, which are much higher than the increases under RCP4.5. The maximum increase of the RegCM driven by CanESM2 (CARM)-projected changes in streamflow for the 2050s and 2080s under RCP4.5 is 0.30 and 0.59 × 103 m s−3 in November, respectively. In addition, in a multimodel GCM–RCM–HM ensemble, hydroclimate is found to be most sensitive to the choice of GCM. Moreover, it is revealed that the percentage of contribution of anthropogenic emissions to the changes in monthly precipitation is relatively smaller, but it makes a more significant contribution to the total variance of changes in potential evapotranspiration and streamflow.

Significance Statement

Increasing concerns have been paid to climate change due to its aggravating impacts on the hydrologic regime, leading to water-related disasters. Such impacts can be investigated through long-term hydrological projection under climate change. However, it is not well understood what factor plays a dominant role in inducing extensive uncertainties associated with the long-term hydrological projections due to plausible meteorological forcings, multiple hydrologic models, and internal variability. The stepwise cluster Bayesian ensemble method and mixed-level factorial inference approach are employed to quantify the contribution of multiple uncertainty sources. We find that the total variance of changes in monthly precipitation, potential evapotranspiration, and streamflow can be mainly explained by the model choices. The identified dominant factor accounting for projection uncertainties is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management. It is suggested that more reliable models should be taken into consideration in order to improve the projection robustness from a perspective of the Loess Plateau.

Restricted access
Mei Hou
,
Lan Cuo
,
Amirkhamza Murodov
,
Jin Ding
,
Yi Luo
,
Tie Liu
, and
Xi Chen

Abstract

Transboundary rivers are often the cause of water related international disputes. One example is the Amu Darya River, with a catchment area of 470,000 km2, that passes through five countries and provides water resource for 89 million people. Intensified human activities and climate change in this region have altered hydrological processes and led to water related conflicts and ecosystem degradation. Understanding streamflow composition and quantifying the change impacts on streamflow in the Amu Darya Basin (ADB) are imperative to water resources management. Here, a degree-day glacier-melt scheme coupled offline with the Variable Infiltration Capacity hydrological model (VIC-glacier), forced by daily precipitation, maximum and minimum air temperature, and wind speed, is used to examine streamflow composition and changes during 1953–2019. Results show large differences in streamflow composition among the tributaries. There is a decrease in snow melt component (−260.8 m3 s−1) and rainfall component (−30.1 m3 s−1) at Kerki but an increase in glacier melt component (160.0 m3 s−1) during drought years. In contrast, there is an increase in snow melt component (378.6 m3 s−1) and rainfall component (12.0 m3 s−1) but a decrease in glacier melt component (−201.8 m3 s−1) during wet years. Using the VIC-glacier and climate elasticity approach, impacts of human activities and climate change on streamflow at Kerki and Kiziljar during 1956–2015 are quantified. Both methods agree and show a dominant role played by human activities in streamflow reduction, with contributions ranging 103.2– 122.1%; however, the contribution of climate change ranges in −22.1– −3.2%.

Restricted access
Tzu-Ying Yang
,
Cho-Ying Huang
,
Jehn-Yih Juang
,
Yi-Ying Chen
,
Chao-Tzuen Cheng
, and
Min-Hui Lo

Abstract

Fog plays a vital role in maintaining ecosystems in montane cloud forests. In these forests, a large amount of water on the surface of leaves and canopy (hereafter canopy water) evaporates during the morning. This biophysical process plays a critical factor in regulating afternoon fog formation. Recent studies have found that alterations in precipitation, temperature, humidity, and CO2 concentrations associated with future climate changes may affect terrestrial hydroclimatology, but the responses in cloud forests remain unclear. Utilizing numerical experiments with the Community Land Model, we explored changes in surface evaporative fluxes in Chi-Lan Mountain cloud forests in northeastern Taiwan under the RCP8.5 scenario with changes in the aforementioned various atmospheric variables. The results showed that increased rainfall intensity in climate change runs decreased the accumulation of canopy water, while larger water vapor concentrations led to more nighttime condensation on leaves. Elevated CO2 concentrations did not greatly impact canopy water amounts, but photosynthesis was enhanced, while transpiration was reduced and contributed to decreased latent heat fluxes, implying the importance of forest plant physiology in modulating land evaporative fluxes. Evapotranspiration decreased in Chi-Lan due to multiple combined factors, in contrast to the expected intensification in the global water cycle under global warming. The study, however, is restricted to an offline land surface model without land–atmosphere interactions and the interactions with adjacent grids, which deserves further analyses for the water cycle changes in the montane cloud forest regions.

Open access
Benjamin Krichman
,
Srinivas Bettadpur
, and
Tatyana Pekker

Abstract

GRACE and GRACE Follow-On (GRACE-FO) mission data are utilized to assess mass flux derived from the North American Regional Reanalysis (NARR) and the NLDAS-2 Noah land surface model via multiple water balance formulations. Water balances are computed for 18 medium size basins in North America at the USGS Watershed Boundary Dataset HU2 level over the span of the GRACE and GRACE-FO missions (2002–21). Performance of model-derived mass flux is presented in the context of statistical agreement to changes in terrestrial water storage (ΔTWS) derived from Center for Space Research (CSR) GRACE RL06 mass concentrations (mascons), and GRACE and NARR uncertainty is estimated against comparable datasets. The land surface water balance method utilizing NLDAS-2 Noah consistently outperforms the total column method utilizing NARR, which is likely due to enhanced precipitation forcing and an updated Noah model version used in NLDAS-2. The surface approach to the calculation of atmospheric moisture flux divergence is carried through the presented analyses and is demonstrated to be comparable in performance to the more common volume approach. Mass balance methodology, basin characteristics, and ΔTWS signal characteristics are assessed to quantify effects on model performance and while factors such as basin size, basin average topography gradient, and ΔTWS annual amplitude are shown to have a measurable effect on model performance, no single factor exhibited a dominant or consistent effect. Drought conditions are shown to have a significant temporally localized effect on model-derived mass flux accuracy, with NARR being particularly susceptible to this effect.

Significance Statement

Measurements of Earth’s gravity field from the GRACE and GRACE-FO satellite missions are utilized to create estimates of water storage changes in 18 North American river basins that are compared to changes in water storage calculated from an atmospheric model reanalysis (NARR) and a land surface model (NLDAS-2 Noah). The resulting comparison demonstrates that certain basin characteristics can have a slight effect on model accuracy, while climatic conditions such as drought can have a major impact on model accuracy. This work provides useful quantification of when and where modeled water transport loses accuracy, which is integral to our understanding of the present and future distribution of this crucial resource and the natural processes that affect it.

Open access