Hao Guo

Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia

This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between −57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%).

Vegetation dynamics and responses to climate change and human activities in Central Asia

Knowledge of the current changes and dynamics of different types of vegetation in relation to climatic changes and anthropogenic activities is critical for developing adaptation strategies to address the challenges posed by climate change and human activities for ecosystems. Based on a regression analysis and the Hurst exponent index method, this research investigated the spatial and temporal characteristics and relationships between vegetation greenness and climatic factors in Central Asia using the Normalized Difference Vegetation Index (NDVI) and gridded high-resolution station (land) data for the period 1984-2013. Further analysis distinguished between the effects of climatic change and those of human activities on vegetation dynamics by means of a residual analysis trend method. The results show that vegetation pixels significantly decreased for shrubs and sparse vegetation compared with those for the other vegetation types and that the degradation of sparse vegetation was more serious in the Karakum and Kyzylkum Deserts, the Ustyurt Plateau and the wetland delta of the Large Aral Sea than in other regions. The Hurst exponent results indicated that forests are more sustainable than grasslands, shrubs and sparse vegetation. Precipitation is the main factor affecting vegetation growth in the Kazakhskiy Melkosopochnik. Moreover, temperature is a controlling factor that influences the seasonal variation of vegetation greenness in the mountains and the Aral Sea basin. Drought is the main factor affecting vegetation degradation as a result of both increased temperature and decreased precipitation in the Kyzylkum Desert and the northern Ustyurt Plateau. The residual analysis highlighted that sparse vegetation and the degradation of some shrubs in the southern part of the Karakum Desert, the southern Ustyurt Plateau and the wetland delta of the Large Aral Sea were mainly triggered by human activities: the excessive exploitation of water resources in the upstream areas of the Amu Darya basin and oil and natural gas extraction in the southern part of the Karakum Desert and the southern Ustyurt Plateau. The results also indicated that after the collapse of the Soviet Union, abandoned pastures gave rise to increased vegetation in eastern Kazakhstan, Kyrgyzstan and Tajikistan, and abandoned croplands reverted to grasslands in northern Kazakhstan, leading to a decrease in cropland greenness. Shrubs and sparse vegetation were extremely sensitive to short-term climatic variations, and our results demonstrated that these vegetation types were the most seriously degraded by human activities. Therefore, regional governments should strive to restore vegetation to sustain this fragile arid ecological environment.

Evaluation of PERSIANN-CDR for Meteorological Drought Monitoring over China

In this paper, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is analyzed for the assessment of meteorological drought. The evaluation is conducted over China at 0.5° spatial resolution against a ground-based gridded China monthly Precipitation Analysis Product (CPAP) from 1983 to 2014 (32 years). The Standardized Precipitation Index (SPI) at various time scales (1 month to 12 months) is calculated for detecting drought events. The results show that PERSIANN-CDR depicts similar drought behavior as the ground-based CPAP in terms of capturing the spatial and temporal patterns of drought events over eastern China, where the intensity of gauge networks and the frequency of droughts are high. 6-month SPI shows the best agreement with CPAP in identifying drought months. However, large differences between PERSIANN-CDR and CPAP in depicting drought patterns and identifying specific drought events are found over northwestern China, particularly in Xinjiang and Qinghai-Tibet Plateau region. Factors behind this may be due to the relatively sparse gauge networks, the complicated terrain and the performance of PERSIANN algorithm.

Understanding the Spatial Temporal Vegetation Dynamics in Rwanda

Knowledge of current vegetation dynamics and an ability to make accurate predictions of ecological changes are essential for minimizing food scarcity in developing countries. Vegetation trends are also closely related to sustainability issues, such as management of conservation areas and wildlife habitats. In this study, AVHRR and MODIS NDVI datasets have been used to assess the spatial temporal dynamics of vegetation greenness in Rwanda under the contrasting trends of precipitation, for the period starting from 1990 to 2014, and for the first growing season (season A). Based on regression analysis and the Hurst exponent index methods, we have investigated the spatial temporal characteristics and the interrelationships between vegetation greenness and precipitation in light of NDVI and gridded meteorological datasets. The findings revealed that the vegetation cover was characterized by an increasing trend of a maximum annual change rate of 0.043. The results also suggest that 81.3% of the country’s vegetation has improved throughout the study period, while 14.1% of the country’s vegetation degraded, from slight (7.5%) to substantial (6.6%) deterioration. Most pixels with severe degradation were found in Kigali city and the Eastern Province. The analysis of changes per vegetation type highlighted that five types of vegetation are seriously endangered: The “mosaic grassland/forest or shrubland” was severely degraded, followed by “sparse vegetation,” “grassland or woody vegetation regularly flooded on water logged soil,” “artificial surfaces” and “broadleaved forest regularly flooded.” The Hurst exponent results indicated that the vegetation trend was consistent, with a sustainable area percentage of 40.16%, unsustainable area of 1.67% and an unpredictable area of 58.17%. This study will provide government and local authorities with valuable information for improving efficiency in the recently targeted countrywide efforts of environmental protection and regeneration.

Spatial and temporal characteristics of droughts in Central Asia during 1966–2015

In drought-prone regions like Central Asia, drought monitoring studies are paramount to provide valuable information for drought risk mitigation. In this paper, the spatiotemporal drought characteristics in Central Asia are analyzed from 1966 to 2015 using the Climatic Research Unit (CRU) dataset. Drought events, as well as their frequency, duration, severity, intensity and preferred season, are studied by using the Run theory and the Standardized Precipitation Evapotranspiration Index (SPEI) at 3-month, 6-month, and 12-month timescales. The Principle Components Analysis (PCA) and the Varimax rotation method, the Sens slope and the Modified Mann-Kendall method (MMK), as well as the wavelet analysis are adopted to identify the sub-regional drought patterns and to study the drought trend, periodicity and the possible links between drought variation and large-scale climate patterns, respectively. Results show that the drought characteristics in Central Asia vary considerably. The Hexi Corridor region and the southeastern part suffered from more short-term drought occurrences which mostly occurred in summer while the northeastern part experienced fewer droughts with longer duration and higher severity. Central Asia showed an overall wetting trend with a switch to drying trend since 2003. Regionally, the continuous wetting trend is found in north Kazakhstan while a consistent drying in the Aral Sea and Hexi Corridor region is observed in the last half-century. For 2003-2015, a significant drying pattern is detected in most Central Asia, except the northern Kazakhstan. A common significant 16-64-month periodical oscillation can be detected over the six sub-regions. The drought changes in Central Asia are highly associated with ENSO but less related to the Tibetan Plateau pressure. The North Atlantic Oscillation has an influence on drought change in most Central Asia but less for the Hexi Corridor and the drought variation in eastern Central Asia is affected by the strength of the Siberian High.

Systematical Evaluation of Satellite Precipitation Estimates Over Central Asia Using an Improved Error‐Component Procedure

Satellite precipitation estimates (SPEs) provide important alternative precipitation sources for various applications especially for regions where in-situ observations are limited or unavailable, like Central Asia. In this study, eight SPEs based on four different algorithms namely the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, Climate Prediction Center (CPC) morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN) are evaluated by using an improved evaluation system over Central Asia with respect to their performance in capturing precipitation occurrence and magnitude. Both satellite-only and gauge-corrected versions are assessed against gauge-gridded reference from June 2001 to May 2006. Main results show that all SPEs have difficulties in accurately estimating mountainous precipitation with great over/underestimation in both winter and summer. In winter, CMORPH products fail to capture events over ice/snow covered region. In summer, large overestimations dominated by positive hit bias and missed precipitation are found for all products in northern Central Asia. Interestingly, 3B42 and CMORPH products show great false alarm percentages (up to 90%) over Lake region, which is more significant in summer than in winter. Significant elevation-dependent errors exist in all products, especially for the high-altitude regions (>3000 m) with missed error and hit error being the two leading errors. Satellite-only products have large systematic and random errors, while the gauge-corrected products demonstrate significant improvements in reducing random errors. Generally, the gauge-corrected GSMaP performs better than others with good skills in reducing various errors.