The warming climate is threatening to alter inland water resources on a global scale. Within all waterbody types, lake and river systems are vital not only for natural ecosystems but, also, for human society. Snowmelt phenology is also altered by global warming, and snowmelt is the primary water supply source for many river and lake systems around the globe. Hence, (1) monitoring snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced river and lake systems, and (3) quantifying the causal effect of snowmelt conditions on these waterbodies are critical to understand the cryo-hydrosphere interactions under climate change. Previous studies utilized in-situ or multispectral sensors to track either the surface areas or water levels of waterbodies, which are constrained to small-scale regions and limited by cloud cover, respectively. On the contrary, in the present study, we employed the latest Sentinel-1 synthetic aperture radar (SAR) and Sentinel-3 altimetry data to grant a high-resolution, cloud-free, and illumination-independent comprehensive inland water dynamics monitoring strategy. Moreover, in contrast to previous studies utilizing in-house algorithms, we employed freely available cloud-based services to ensure a broad applicability with high efficiency. Based on altimetry and SAR data, the water level and the water-covered extent (WCE) (surface area of lakes and the flooded area of rivers) can be successfully measured. Furthermore, by fusing the water level and surface area information, for Lake Urmia, we can estimate the hypsometry and derive the water volume change. Additionally, for the Brahmaputra River, the variations of both the water level and the flooded area can be tracked. Last, but not least, together with the wet snow cover extent (WSCE) mapped with SAR imagery, we can analyze the influence of snowmelt conditions on water resource variations. The distributed lag model (DLM) initially developed in the econometrics discipline was employed, and the lagged causal effect of snowmelt conditions on inland water resources was eventually assessed.

中文翻译：

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通过融合Sentinel-1 SAR和Sentinel-3高程数据并分析融雪的因果效应来监测大规模内陆水动力学

气候变暖正威胁着全球范围内陆水资源的变化。在所有水体类型中，湖泊和河流系统不仅对自然生态系统至关重要，而且对人类社会也至关重要。全球变暖也改变了融雪的物候，融雪是全球许多河流和湖泊系统的主要水源。因此，（1）监测融雪状况，（2）跟踪受融雪影响的河流和湖泊系统的动力学，以及（3）量化融雪状况对这些水体的因果关系，对于理解气候变化下的冰冻水圈相互作用至关重要。 。先前的研究利用原位或多光谱传感器来跟踪水体的表面积或水位，水体的表面积或水位分别限制在小范围内并且受云层的限制。相反，在本研究中，我们采用了最新的Sentinel-1合成孔径雷达（SAR）和Sentinel-3测高仪数据来授予高分辨率，无云和不依赖照明的全面内陆水动力学监测策略。此外，与以前利用内部算法进行的研究相比，我们采用了免费的基于云的服务来确保高效的广泛适用性。根据测高和SAR数据，可以成功测量水位和水覆盖范围（WCE）（湖泊的表面积和河流的淹没面积）。此外，通过融合水位和表面积信息，对于乌尔米亚湖，我们可以估算水势法并得出水量变化。另外，对于雅鲁藏布江，可以跟踪水位和洪水区域的变化。最后但并非最不重要的一点是，结合SAR影像绘制的湿雪覆盖范围（WSCE），我们可以分析融雪条件对水资源变化的影响。使用了最初在计量经济学学科中开发的分布式滞后模型（DLM），并最终评估了融雪条件对内陆水资源的滞后因果关系。