Search

Under the context of global warming, compound dry and hot events (CDHEs) will increase and bring serious losses to society and the economy. The projection of CDHEs is of great significance for policy-making and risk assessment. In this paper, two large ensemble simulations, CanESM2-LE and CESM-LE, are used to estimate the risk of extreme CDHEs under different warming scenarios in China. First, the biases of the model in the simulation of the temperature and precipitation over the China region are corrected, and the index of CDHEs is established based on a copula function. The results show that extreme CDHEs will occur more often in China with the increase in global warming and the more severe extreme CDHEs are, the greater the risk will be in the future with higher uncertainties. Events that would be attained once every 50 and 100 years in the current climate from CESM-LE (CAanESM2-LE) will be 1.2/1.6 (1.1/1.5) times and 1.3/2.3 (1.5/2.0) times more likely to occur in a 1.5°C/2.0°C warmer climate, respectively. Northwestern China will experience the greatest increase in the risk of extreme CDHEs. Extreme CDHEs expected once every 100 years in the current period over NW China are expected to occur approximately every 5 and 4 years under a 4.0°C warmer world in CanESM2-LE and CESM-LE, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Obtaining high-quality quantitative precipitation forecasts is a key precondition for hydrological forecast systems. Due to multisource uncertainties (e.g., initial conditions, model structures, and parameters), raw forecasts are subject to systematic biases; hence, statistical postprocessing is often required to reduce these errors before the forecasts can proceed to hydrological applications. Machine learning (ML) algorithms are canonical statistical models, and they are diverse in type and variation. It is important to verify and compare their performance in the same scenario (e.g., precipitation postprocessing). In this paper, we conduct a large-scale comparison study for the major ML models with diverse model structures and regularization strategies as postprocessors for improving the quality of precipitation forecasts. Specifically, we compare the efficiency and effectiveness of 21 ML algorithms on solving this task. Daily reforecast precipitation with lead times up to 8 days from the Global Ensemble Forecast System and corresponding observations are employed to determine the usability of different models in the Yalong River basin in China. The performance of each model is validated by a group of carefully designed experiments and statistical metrics. The results reveal that improvements in model structures are more effective than regularization strategies. Among these algorithms, the optimized extra-trees regressor exhibits the best performance, effectively reducing overestimation and achieving the best skill in forecasting precipitation. Eleven ensemble members and a 3-day forward-rolling time window can be used as predictors to obtain the best model performance. The systematic experiments and findings also offer useful guidelines for other related studies. [ABSTRACT FROM AUTHOR]

ABSTRACT The biases in the Global Circulation Models ( GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree ( RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970-1999. [ABSTRACT FROM AUTHOR]

Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River basin, northwestern China, and expected to be vulnerable to climate change. It has been demonstrated that regional climate models (RCMs) provide more reliable results for a regional impact study of climate change (e.g., on water resources) than general circulation models (GCMs). However, due to their considerable bias it is still necessary to apply bias correction before they are used for water resources research. In this paper, after a sensitivity analysis on input meteorological variables based on the Sobol' method, we compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations applied over the Kaidu River basin, one of the headwaters of the Tarim River basin. Precipitation correction methods applied include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and quantile mapping (QM), while temperature correction methods are LS, variance scaling (VARI) and DM. The corrected precipitation and temperature were compared to the observed meteorological data, prior to being used as meteorological inputs of a distributed hydrologic model to study their impacts on streamflow. The results show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCM simulations are heavily biased from observed meteorological data, and its use for streamflow simulations results in large biases from observed streamflow, and all bias correction methods effectively improved these simulations; (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g., standard deviation, percentile values) while the LOCI method performed best in terms of the time-series-based indices (e.g., Nash-Sutcliffe coefficient, R2); (4) for temperature, all correction methods performed equally well in correcting raw temperature; and (5) for simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with those of corrected precipitation; i.e., the PT and QM methods performed equally best in correcting flow duration curve and peak flow while the LOCI method performed best in terms of the time-series-based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other areas and models. [ABSTRACT FROM AUTHOR]

Scarcity or unavailability of precipitation observation creates difficulties in hydrologic modeling of mountainous sections of the arid region of northwest China (34°-50°N, 72°-107°E). Tropical Rainfall Measuring Mission (TRMM) precipitation products may be a potential substitute, but they should be evaluated and corrected with ground observation data before application. In this paper, two TRMM Multisatellite Precipitation Analysis (TMPA) precipitation products were evaluated by gauge observations, using indices such as frequency bias index, probability of detection, false alarm ratio, relative mean bias, Nash-Sutcliffe efficiency, and correlation coefficient. Terrain variables were extracted from a digital elevation model, and their rotated principal components were determined to establish a stepwise regression model to adjust TMPA precipitation. Additionally, a back-propagation (BP) neural network was established to correct TMPA precipitation. The results showed that TMPA had an unsatisfactory detection ability in the study area for both precipitation occurrence and amount. TMPA precipitation corrected by a stepwise regression method showed some improvement, but only the results for TRMM 3B43 on a subregion scale were acceptable. The BP neural network method showed better results than the stepwise regression method, and both TRMM 3B42 and TRMM 3B43 corrected by the former method on a subregion scale could be acceptable. Both methods were spatial-scale dependent and showed better results on a subregion scale than on a larger scale. [ABSTRACT FROM AUTHOR]

• A Bernoulli-Gamma-Gaussian model is built to calibrate S2S precipitation forecasts. • The model is effective in correcting bias and improving forecast reliability and skill. • A 15-day sliding window is suitable for pooling samples for S2S forecast calibration. Subseasonal to seasonal (S2S) precipitation forecasts can fill the gap between weather and seasonal forecasts. While raw forecasts from S2S models are informative, calibration is necessary to correct systematic biases and quantify forecast uncertainty to facilitate applications of S2S forecasts. This paper develops a seven-parameter Bernoulli-Gamma-Gaussian model to calibrate S2S precipitation forecasts. The Bernoulli distribution characterizes the occurrence of zero versus non-zero values of precipitation, the Gamma distribution accounts for the distribution of non-zero precipitation amounts and the bivariate Gaussian distribution formulates the relationship between raw forecasts and observations. A case study of the East River catchment in South China is devised for the S2S forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECWMF). The Bernoulli-Gamma-Gaussian model is used to calibrate raw forecasts at the daily time step and the Schaake Shuffle is used to instill the temporal patterns to form ensemble time series forecasts. The results show that raw S2S forecasts generally suffer from considerable forecast bias and unreliable ensemble spread and can exhibit negative forecast skill. Compared to the quantile mapping, the Bernoulli-Gamma-Gaussian model with the Schaake Shuffle is effective in correcting biases, improving reliability and yielding forecast skill for daily and accumulated precipitation. Furthermore, a sliding window of 15 days tends to be suitable to pool samples to deal with sampling variability and precipitation seasonality for the purpose of calibration of S2S forecasts. Overall, the Bernoulli-Gamma-Gaussian model can serve as an effective tool to calibrate S2S precipitation forecasts for hydrological modeling and water resources management. [ABSTRACT FROM AUTHOR]

In this study, an innovative MODIS fractional snow cover (SCF) data assimilation (DA) prototype framework that invokes machine learning (ML) techniques and Common land model (CoLM) is proposed to improve the estimation of the snow depth (SD) and the SCF. To validate our new framework, we analyzed two snow seasons from 2013 to 2015 at 46 stations in Northern Xinjiang in China. We developed 12 SCF DA schemes that represent different DA methods (direct insertion (DI) and Ensemble Kalman Filter (EnKF)), observational data (original data and gap‐filled MODIS SCF data), and observation operators (five new snow depletion curves (SDCs) defined using traditional multivariate nonlinear regression and four ML methods). While improving the frequency of the SCF observations in the DI‐based DA scheme only resulted in a marginal improvement in the snow estimates, by adding new SDCs fitted by ML techniques (e.g., deep belief network), and the gap‐filled MODIS SCF data to the EnKF‐based DA scheme, we were able to reduce model structural uncertainties of CoLM and achieve marked improvement in the accuracy of the snow estimates (RMSE = 5.92 cm, mean bias error = −1.94 cm, and average degree of improvement = 32.18% for SD estimates and RMSE = 15. 79%, mean bias error = −1.21%, and average degree of improvement = 47.95% for SCF estimates). Our results demonstrate the feasibility of improving snow estimates by combining the ML techniques with physically based snowpack model in a SCF DA framework. Key Points: An innovative MODIS fractional snow cover assimilation framework invokes machine learning techniques and Common Land Model is proposedFive new snow depletion curves (SDCs) are defined by using multiple nonlinear regression and four machine learning techniquesNew SDCs functions as both the improved parameterization scheme of Common Land Model and the observation operator of assimilation frame [ABSTRACT FROM AUTHOR]

The need for the statistical stability of data is increasing nowadays as the data resource has become a more and more important production factor. In this study, a set of general identification and correction models are established for data outlier modification. The research object we chose is the data of per capita energy consumption. Based on the joint diagnosis method of outliers and the regional convergence theory, the abrupt outliers are identified and corrected. The study finds that there is an outlier in the data of the Ningxia Hui Autonomous Region. According to the club grouping method, 30 provinces in China are divided into two clubs and the Ningxia Hui Autonomous Region is determined to be in the first club. We calculate the convergence rate and obtain the correction results combining the half-life cycle model. [ABSTRACT FROM AUTHOR]

The reduction of uncertainty in simulations and projections of regional climate models is a critical issue for regional climate impact studies, especially in the context of climate extremes. In this study, the regional climate model COSMO-CLM (CCLM) is evaluated in terms of daily precipitation and temperature characteristics, in order to obtain reliable projections of climate extremes for the Tarim River Basin (TRB) in Northwest China. The results show that CCLM can acceptably reproduce the annual cycle of maximum and minimum temperature, as well as the spatial distribution of precipitation pattern. Nonetheless, some systematic biases have been encountered. The equidistant cumulative distribution function matching method has been applied, which led to an efficient reduction of the systematic biases of observed and simulated climate variables. The bias correction has further been applied to climate projections for the period of 2016-2035 under the Representative Concentration Pathway 4.5. The projected indices of climate extremes as calculated from the bias-corrected CCLM projections show that most of the TRB is likely to experience a decrease in daily temperature range, and an increase in minimum temperature as well as consecutive wet days. The total precipitation on very wet days is projected to slightly increase at most stations, while the annual total precipitation will mostly increase in the southwestern parts of the TRB. The findings on the spatial-temporal patterns of these climate extremes will enable decision makers, especially in the water and agricultural sectors, to adapt and be better prepared for future climate impacts in the region. [ABSTRACT FROM AUTHOR]

The performances of various dynamical models from the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) in predicting station-scale rainfall in South China (SC) in June were evaluated. It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain. This could be related to the MME's ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region. Further assessment of station-scale June rainfall prediction based on direct model output (DMO) over 97 stations in SC revealed that the MME mean outperforms each individual model. However, poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models. In order to improve the performance at those stations with poor DMO prediction skill, a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs. For several models, this scheme can outperform DMO at more than 30 stations, because it can tap into the abilities of the models in capturing the anomalous Indo-Pacific circulation to which SC rainfall is considerably sensitive. Therefore, enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies. [ABSTRACT FROM AUTHOR]

On 31 July 2020, the Beidou global navigation satellite system (BDS-3) was officially announced as being commissioned. In addition to offering global positioning, navigation, and timing (PNT) services, BDS-3 also provides precise point positioning (PPP) augmentation services. The satellite orbit correction, clock correction and code bias correction of BDS-3 and other global navigation satellite systems (GNSS) are broadcast by the BDS-3 geostationary earth orbit (GEO) satellites through the PPP-B2b signal. The PPP-B2b service is available for users in China and the surrounding area. In this study, an initial assessment of the PPP-B2b service is presented, with collected 3-day PPP-B2b messages. Based on broadcast ephemeris and PPP-B2b messages, the precise satellite orbits and clock offsets can be recovered. This precision is evaluated with the precise ephemeris from the GeoForschungsZentrum Potsdam (GFZ) analysis center as references. The results indicate that the accuracy of BDS-3 satellite orbits in the direction of radial, along-track, and cross-track is 0.138, 0.131, and 0.145 m, respectively, and for GPS a corresponding accuracy of 0.104, 0.160, and 0.134 m, respectively, could be obtained. The precision of clock offsets can reach a level of several centimeters for both GPS and BDS-3. Both the performance of static PPP and kinematic PPP are evaluated using the observations from four international GNSS monitoring assessment service (iGMAS) stations. Regarding static PPP, the average convergence time is 17.7 min to achieve a horizontal positioning accuracy of better than 0.3 m, and a vertical positioning accuracy of better than 0.6 m. The average positioning accuracy in the direction of east, north, and up-directions are 2.4, 1.6, and 2.3 cm. As to kinematic PPP, the average RMS values of positioning errors in the direction of east, north, and up are 8.1 cm, 3.6 cm, and 8.0 cm after full convergence. [ABSTRACT FROM AUTHOR]

Availability of precipitation data at high spatial and temporal resolution is crucial for the understanding of precipitation behaviors that are determinant for environmental aspects such as hydrology, ecology, and social aspects like agriculture, food security, or health issues. This study evaluates the performance of 3B42-V7 satellite-based precipitation product on extreme precipitation estimates in China, by using the Fuzzy C-Means algorithm and L-moment-based regional frequency analysis method. The China Gauge-based Daily Precipitation Analysis (CGDPA) product is employed to measure the estimation biases of 3B42-V7. Results show that: (1) for most regions of China, the Generalized Extreme Value and Generalized Normal distributions are preferable for extreme precipitation estimates; (2) the extreme precipitation estimations of 3B42-V7 for different return periods have a high correlation with those of CGDPA, with biases within 25% for a majority of China on extreme precipitation estimates. [ABSTRACT FROM AUTHOR]

Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d−1, −0.16 mm d−1, and 0.65 mm d−1, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change. [ABSTRACT FROM AUTHOR]

Recently, the government installed a boundary layer profiler (BLP), which is operated under the Doppler beam swinging mode, in a coastal area of China, to acquire useful wind field information in the atmospheric boundary layer for several purposes. And under strong wind conditions, the performance of the BLP is evaluated. It is found that, even though the quality controlled BLP data show good agreement with the balloon observations, a systematic bias can always be found for the BLP data. For the low wind velocities, the BLP data tend to overestimate the atmospheric wind. However, with the increment of wind velocity, the BLP data show a tendency of underestimation. In order to remove the effect of poor quality data on bias correction, the probability distribution function of the differences between the two instruments is discussed, and it is found that the t location scale distribution is the most suitable probability model when compared to other probability models. After the outliers with a large discrepancy, which are outside of 95% confidence interval of the t location scale distribution, are discarded, the systematic bias can be successfully corrected using a first-order polynomial correction function. The methodology of bias correction used in the study not only can be referred for the correction of other wind profiling radars, but also can lay a solid basis for further analysis of the wind profiles. [ABSTRACT FROM AUTHOR]

Investigating the impact of climate change at regional scales is of importance for adaption purposes. In this study, a regional climate model PRECIS is for the first time run for East China on a spatial resolution of 25 km using two global climate models (GCMs) ECHAM5 and HadCM3 as boundary data under A1B emission scenario. The baseline period is 1961-1990. An upgraded quantile mapping method is used to correct the bias of PRECIS outputs for providing reliable future climate change projections (2011-2040). The regional climate model is then coupled with a mesoscale distributed hydrology soil vegetation model (DHSVM), to investigate the impact of climate change on future water resources in the Jinhua River Basin, East China. The hydrological model is calibrated and validated on a daily basis and show reasonable performance. The results show that under both GCMs, statistically, no significant changes in future annual runoff are projected. However, large changes in seasonal and monthly runoff are projected. ECHAM5 projects large decreases in summer () and autumn () and slight increases in spring and winter whereas HadCM3 projects large decreases in autumn () and large increases in spring (28.7%) and winter (60%). The seasonal changes in precipitation in this region are the main driving force for the changes in future runoff. [ABSTRACT FROM AUTHOR]