[1] Changes in regional groundwater storage in North China were estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites data and ground-based measurements collected from 2003 to 2010. The study area (∼370,000 km2) included the Beijing and Tianjin municipality, the Hebei and Shanxi province, which is one of the largest irrigation areas in the world and is subjected to intensive groundwater-based irrigation. Groundwater depletion in North China was estimated by removing the simulated soil moisture changes from the GRACE-derived terrestrial water storage changes. The rate of groundwater depletion in North China based on GRACE was 2.2 ± 0.3 cm/yr from 2003 to 2010, which is equivalent to a volume of 8.3 ± 1.1 km3/yr. The groundwater depletion rate estimated from monitoring well stations during the same time period was between 2.0 and 2.8 cm/yr, which is consistent with the GRACE-based result. However, the estimated groundwater depletion rate in shallow plain aquifers according to the Groundwater Bulletin of China Northern Plains (GBCNP) for the same time period was only approximately 2.5 km3/yr. The difference in groundwater depletion rates estimated from GRACE and GBCNP data indicates the important contribution of groundwater depletion from deep aquifers in the plain and piedmont regions of North China.

Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground-based measurements (EI)

Summary The post-real time product of Day-1 Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) is evaluated over Mainland China from April to December 2014 at the hourly timescale, against data from hourly ground-based observations. In addition, the IMERG product is compared with its predecessor-the Version-7 post-real-time 3B42 (3B42V7) product of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) at its original 3-hourly and then daily timescales for the same period. All the products are cross-evaluated at gridded, regional, and national scales. Results show that: (1) the Day-1 IMERG shows appreciably better performance than 3B42V7 at both sub-daily and daily timescales, and all the three spatial scales. The gap between the two products is more significant at the sub-daily resolution; (2) Out of the six sub-regions of China, IMERG especially performs better than 3B42V7 at the mid- and high-latitudes, as well as relatively dry climate regions; (3) IMERG can better reproduce the probability density function (PDF) in terms of precipitation intensity, particularly in the low ranges; and (4) although IMERG better captures the precipitation diurnal variability, both products have room to further improve their capability, particularly in the dry climate and high-latitude regions. This study is among the earliest evaluation and comparison of IMERG and 3B42V7 products, which could be valuable in providing reference for the development of IMERG algorithms, associated global products, and various applications as well.

First-Year Evaluation of GPM Day-1 IMERG and TMPA Version-7 Legacy Products over Mainland China at Multiple Spatiotemporal Scales

Operational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble flood forecasting’ and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the ‘added value’ of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.

Ensemble flood forecasting: A review

. The climate modelling community has trialled a large
number of metrics for evaluating the temporal performance of general circulation
models (GCMs), while very little attention has been given to the assessment
of their spatial performance, which is equally important. This study
evaluated the performance of 36 Coupled Model Intercomparison Project 5
(CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon,
winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum
temperature over Pakistan using state-of-the-art spatial metrics, SPAtial
EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V,
Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The
multi-model ensemble (MME) precipitation and maximum and minimum temperature
data were generated through the intelligent merging of simulated
precipitation and maximum and minimum temperature of selected GCMs employing
random forest (RF) regression and simple mean (SM) techniques. The results indicated
some differences in the ranks of GCMs for different spatial metrics. The
overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the
best GCMs in simulating the spatial patterns of mean annual, monsoon,
winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum
temperature over Pakistan. MME precipitation and maximum and minimum
temperature generated based on the best-performing GCMs showed more
similarities with observed precipitation and maximum and minimum temperature
compared to precipitation and maximum and minimum temperature simulated by
individual GCMs. The MMEs developed using RF displayed better performance
than the MMEs based on SM. Multiple spatial metrics have been used for the
first time for selecting GCMs based on their capability to mimic the spatial
patterns of annual and seasonal precipitation and maximum and minimum
temperature. The approach proposed in the present study can be extended to
any number of GCMs and climate variables and applicable to any region for
the suitable selection of an ensemble of GCMs to reduce uncertainties in
climate projections.

/pdf/selection-of-multi-model-ensemble-of-general-circulation-476wpytxxf.pdf

Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics

The performance of general circulation models (GCMs) in a region are generally assessed according to their capability to simulate historical temperature and precipitation of the region. The performance of 31 GCMs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) is evaluated in this study to identify a suitable ensemble for daily maximum, minimum temperature and precipitation for Pakistan using multiple sets of gridded data, namely: Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Berkeley Earth Surface Temperature (BEST), Princeton Global Meteorological Forcing (PGF) and Climate Prediction Centre (CPC) data. An entropy-based robust feature selection approach known as symmetrical uncertainty (SU) is used for the ranking of GCM. It is known from the results of this study that the spatial distribution of best-ranked GCMs varies for different sets of gridded data. The performance of GCMs is also found to vary for both temperatures and precipitation. The Commonwealth Scientific and Industrial Research Organization, Australia (CSIRO)-Mk3-6-0 and Max Planck Institute (MPI)-ESM-LR perform well for temperature while EC-Earth and MIROC5 perform well for precipitation. A trade-off is formulated to select the common GCMs for different climatic variables and gridded data sets, which identify six GCMs, namely: ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES and MIROC5 for the reliable projection of temperature and precipitation of Pakistan.

https://www.mdpi.com/2073-4441/10/12/1793/pdf

Performance assessment of general circulation model in simulating daily precipitation and temperature using multiple gridded datasets

Influences of climatic variability and human activities on terrestrial water storage variations across the Yellow River basin in the recent decade

Role of satellite and reanalysis precipitation products in streamflow and sediment modeling over a typical alpine and gorge region in Southwest China

Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs.

Haiting Gu

Papers

Influences of climatic variability and human activities on terrestrial water storage variations across the Yellow River basin in the recent decade

Role of satellite and reanalysis precipitation products in streamflow and sediment modeling over a typical alpine and gorge region in Southwest China

Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province, China

How well do the ERA-Interim, ERA-5, GLDAS-2.1 and NCEP-R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau?

A surrogate model for the Variable Infiltration Capacity model using deep learning artificial neural network