Integrated hydrologic modeling in the inland Heihe River Basin, Northwest China

As a typical inland river basin in arid Northwest China, having distinct hydrological characteristics and severe and repre- sentative water problems, the Heihe River Basin （HRB） has attracted considerable research interest worldwide and in 2007 became a pilot basin of the G-WADI network of UNESCO/1HR Many research programs have been conducted in the HRB since the 1980s, producing rich knowledge and data about the basin, which will be very helpful to further studies. This paper reviews research efforts related to hydrologic modeling and ongoing model integration studies performed in the HRB in re- cent years. Recently, an observation network covering the whole area and a Web-based data-sharing system have been estab- lished which can greatly improve data acquisition. This paper tabulates modeling activities in past years, including model ap- plications, model modifications and enhancements, and model coupling efforts. Also described is a preliminary modeling in- tegration tool designed to quickly build new models, which has been developed for hydrologic modeling purposes. Challeng- es and issues confronted in current studies are discussed, pointing toward key research directions in the future.

Automated and refined wetland mapping of Dongting Lake using migrated training samples based on temporally dense Sentinel 1/2 imagery

Wetlands provide vital ecological services for both humans and environment,necessitating continuous,refined and up-to-date mapping of wetlands for conservation and management.in this study,we developed an automated and refined wetland mapping framework integrating training sample migration method,supervised machine learning and knowledge-driven rules using Google Earth Engine(GEE)platform and open-source geospatial tools.We applied the framework to temporally dense Sentinel-1/2 imagery to produce annual refined wetland maps of the Dongting Lake Wetland(DLW)during 2015-2021.First,the continuous change detection(CCD)algorithm was utilized to migrate stable training samples.Then,annual 10 m preliminary land cover maps with 9 classes were produced using random forest algorithm and migrated samples.Ultimately,annual 10 m refined wetland maps were generated based on preliminary land cover maps via knowledge-driven rules from geometric features and available water-related inventories,with Overall Accuracy(OA)ranging from 81.82%(2015)to 93.84%(2020)and Kappa Coefficient(KC)between 0.73(2015)and 0.91(2020),demonstrating satisfactory performance and substantial potential for accurate,timely and type-refined wetland mapping.Our methodological framework allows rapid and accurate monitoring of wetland dynamics and could provide valuable information and methodological support for monitoring,conservation and sustainable development of wetland ecosystem.

Spatiotemporal changes in vegetation net primary productivity in the arid region of Northwest China, 2001 to 2012

Net primary productivity （NPP） is recognized as an important index of ecosystem conditions and a key variable of the terrestrial carbon cycle. It also represents the comprehensive effects of climate change and anthropogenic activity on terrestrial vegetation. In this study, the temporal-spatial pattern of NPP for the period 2001-2012 was analyzed using a remote sensing-based carbon model （i.e., the Carnegie-Ames-Stanford Approach, CASA） in addition to other methods, such as linear trend analysis, standard deviation, and the Hurst index. Temporally, NPP showed a significant increasing trend for the arid region of Northwest China （ARNC）, with an annual increase of 2.327 g C. Maximum and minimum productivity values appeared in July and December, respectively. Spatially, the NPP was relatively stable in the temperate and warmtemperate desert regions of Northwest China, while temporally, it showed an increasing trend. However, some attention should be given to the northwestern warm-temperate desert region, where there is severe continuous degradation and only a slight improvement trend.