Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Lake dynamics in Central Asia in the past 30 years

Water is a key resource in arid Central Asia (CA) and is heavily affected by climate change and human activities. Temperature across the region has increased drastically especially in the mountain region while precipitation change is less homogeneous. The increased temperature has caused increased melting of glacier and snow which has a large contribution to the runoff in rivers. Human activities such as agriculture irrigation and reservoir management also affect water availability. In the Soviet era, agriculture in CA expanded continuously and large amount of water was extracted from rivers for irrigation. This has caused the catastrophic decline of the Aral Sea. In the post-Soviet era, countries in CA have reorganized their agriculture structure to be self-sufficient. It is important to understand how these changes affect water availability in CA especially under climate change. This dissertation uses lakes as proxy indicators of water availability and assesses how climate and human activities have affected lakes in CA. Seventeen lakes located in three former Soviet republics and western China from seven basins are examined using remote sensing and hydrologic modeling to estimate their changes in area, water level and volume. Agriculture area changes in these basins from seven countries are also examined using remote sensing. It is found that 1) lakes located in the mountains have generally expanded due to the melting glaciers and snow; 2) lakes located in the lowlands have remained relatively stable due to the relative stability of agriculture area; 3) reservoirs exhibit different seasonal patterns due to their major function as power generation reservoirs release water during the winter while irrigation reservoirs release water during the summer; 4) agriculture area in the former Soviet Central Asia republics is highly dependent on precipitation due to the lack of efficient irrigation infrastructure while agriculture in China has continuously expanded due to the adoption of drip irrigation and groundwater extraction. In conclusion, climate is the more dominant factor affecting water availability especially in the mountains causing the lakes to expand while agriculture irrigation has offset some of the surplus in the lowlands causing the lakes to remain relatively stable

Modelling hydrological responses to climate change in a data-scarce semiarid basin in the Tianshan Mountains

The Tianshan Mountains, known as the Water tower of Central Asia, are an essential freshwater source for downstream rivers, residents, irrigation agriculture and ecosystems in Central Asia. Climate in this semiarid region has changed from warm-dry to warm-wet in the last decades. Water resources are highly sensitive to climate change, and their availability is expected to become more unstable in the future. However, hydrological processes in glacierized basins are poorly known due to data scarcity and complex snow and glacier melt dynamics. It is therefore particularly important to improve our understanding of climate change and its impacts on hydrological regimes in dry, glacierized mountain regions such as the Tianshan Mountains. Emphasizing the research gaps related to the scarcity of observational data and lack of understanding of climate variability and hydrological regimes, the aim of this thesis is to model hydrological responses to climate change in a data-scarce glacierized basin in the Tianshan Mountains to better understand mountain hydrology. Spatio-temporal changes of climate change, climate extremes, hydrological and cryosphere were summarized based on data analysis and literature review. ..

Improved atmospheric modelling of the oasis-desert system in Central Asia using WRF with actual satellite products

Because of the use of outdated terrestrial datasets, regional climate models (RCMs) have a limited ability to accurately simulate weather and climate conditions over heterogeneous oasis-desert systems, especially near large mountains. Using actual terrestrial datasets from satellite products for RCMs is the only possible solution to the limitation; however, it is impractical for long-period simulations due to the limited satellite products available before 2000 and the extremely time- and labor-consuming processes involved. In this study, we used the Weather Research and Forecasting (WRF) model with observed estimates of land use (LU), albedo, Leaf Area Index (LAI), and green Vegetation Fraction (VF) datasets from satellite products to examine which terrestrial datasets have a great impact on simulating water and heat conditions over heterogeneous oasis-desert systems in the northern Tianshan Mountains. Five simulations were conducted for 1-31 July in both 2010 and 2012. The decrease in the root mean squared error and increase in the coefficient of determination for the 2 m temperature (T2), humidity (RH), latent heat flux (LE), and wind speed (WS) suggest that these datasets improve the performance of WRF in both years; in particular, oasis effects are more realistically simulated. Using actual satellite-derived fractional vegetation coverage data has a much greater effect on the simulation of T2, RH, and LE than the other parameters, resulting in mean error correction values of 62%, 87%, and 92%, respectively. LU data is the primary parameter because it strongly influences other secondary land surface parameters, such as LAI and albedo. We conclude that actual LU and VF data should be used in the WRF for both weather and climate simulations

Response of hydrological processes to input data in high alpine catchment : an assessment of the Yarkant River basin in China

Most studies of input data used in hydrological models have focused on flow; however, point discharge data negligibly reflect deviations in spatial input data. To study the effects of different input data sources on hydrological processes at the catchment scale, eight MIKE SHE models driven by station-based data (SBD) and remote sensing data (RSD) were implemented. The significant influences of input variables on water components were examined using an analysis of the variance model (ANOVA) with the hydrologic catchment response quantified based on different water components. The results suggest that compared with SBD, RSD precipitation resulted in greater differences in snow storage in the different elevation bands and RSD temperatures led to more snowpack areas with thinner depths. These changes in snowpack provided an appropriate interpretation of precipitation and temperature distinctions between RSD and SBD. For potential evapotranspiration (PET), the larger RSD value caused less plant transpiration because parameters were adjusted to satisfy the outflow. At the catchment scale, the spatiotemporal distributions of sensitive water components, which can be defined by the ANOVA model, indicate that this approach is rational for assessing the impacts of input data on hydrological processes

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products