Evaluating the uncertainty of terrestrial water budget components over High Mountain Asia

Development of a multidecadal land reanalysis over High Mountain Asia

Anthropogenic and climatic changes affect the water and energy cycles in High Mountain Asia (HMA), home to over two billion people and the largest reservoirs of freshwater outside the polar zone. Despite their significant importance for water management, consistent and reliable estimates of water storage and fluxes over the region are lacking because of the high uncertainties associated with the estimates of atmospheric conditions and human management. Here, we relied on multivariate data assimilation (MVDA) to provide estimates of energy and water storage and fluxes that reflect the processes occurring in the region such as greening and irrigation-driven groundwater depletion. We developed and employed an ensemble precipitation estimate by blending different precipitation products thereby reducing the uncertainties and inconsistencies associated with precipitation in HMA. Then, we assimilated five variables that capture the changes in hydrology in response to climate change and anthropogenic activities. Overall, our results have shown that MVDA has allowed a better representation of the land surface processes including greening and irrigation-driven groundwater depletion in HMA.

Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models

The high mountain regions of Asia contain more glacial ice than anywhere on the planet outside of the polar regions. Because of the large population living in the Indus watershed region who are reliant on melt from these glaciers for fresh water, understanding the factors that affect glacial melt along with the impacts of climate change on the region is important for managing these natural resources. While there are multiple climate data products (e.g., reanalysis and global climate models) available to study the impact of climate change on this region, each product will have a different amount of skill in projecting a given climate variable, such as precipitation. In this research, we develop a spatially varying mixture model to compare the distribution of precipitation in the High Mountain Asia region as produced by climate models with the corresponding distribution from in situ observations from the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) data product. Parameter estimation is carried out via a computationally efficient Markov chain Monte Carlo algorithm. Each of the estimated climate distributions from each climate data product is then validated against APHRODITE using a spatially varying Kullback-Leibler divergence measure. Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00515-0.

Irrigation and warming drive the decreases in surface albedo over High Mountain Asia

Human and climate induced land surface changes resulting from irrigation, snow cover decreases, and greening impact the surface albedo over High Mountain Asia (HMA). Here we use a partial information decomposition approach and remote sensing data to quantify the effects of the changes in leaf area index, soil moisture, and snow cover on the surface albedo in HMA, home to over a billion people, from 2003 to 2020. The study establishes strong evidence of anthropogenic agricultural water use over irrigated lands (e.g., Ganges-Brahmaputra) which causes the highest surface albedo decreases (≤ 1%/year). Greening and decreased snow cover from warming also drive changes in visible and near-infrared surface albedo in different areas of HMA. The significant role of irrigation and greening in influencing albedo suggests the potential of a positive feedback cycle where albedo decreases lead to increased evaporative demand and increased stress on water resources.

Revealing four decades of snow cover dynamics in the Hindu Kush Himalaya

Knowledge about the distribution and dynamics of seasonal snow cover (SSC) is of high importance for climate studies, hydrology or hazards assessment. SSC varies considerably across the Hindu Kush Himalaya both in space and time. Previous studies focused on regional investigations or the influence of snow melt on the local hydrological system. Here, we present a systematic assessment of metrics to evaluate SSC dynamics for the entire HKH at regional and basin scale based on AVHRR GAC data at a 0.05° spatial and daily temporal resolution. Our findings are based on a unique four-decade satellite-based time series of snow cover information. We reveal strong variability of SSC at all time scales. We find significantly decreasing SSC trends in individual summer and winter months and a declining tendency from mid-spring to mid-fall, indicating a shift in seasonality. Thanks to this uniquely spatio-temporally resolved long-term data basis, we can particularly highlight the unique temporally variable character of seasonal snow cover and its cross-disciplinary importance for mountain ecosystems and downstream regions.

Trends and Changes in Hydrologic Cycle in the Huanghuaihai River Basin from 1956 to 2018

The Huanghuaihai River Basin (HRB) is one of the most prominent areas of water resource contradiction in China. It is of great significance to explore the relationship between water balance in this area for a deep understanding of the response of the water cycle to climate change. In this study, machine learning methods are used to prolong the actual evapotranspiration (ET) of the basin on the time scale and explore water balances calculated from various sources. The following conclusions are obtained: (1) it is found that the simulation accuracy of Global Land Evaporation Amsterdam Model (GLEAM) products in HRB is good. The annual average ET spatial distribution tends to increase from northwest to southeast; (2) three machine learning algorithms are used to construct the ET calculation model. The correlation coefficients of the three methods are all above 0.9 and the mean relative error values of random forest (RF) are all less than 30%. The RF has the best effect; (3) the relative errors of water balance in HRB from 1956–1979, 1980–2002 and 2003–2018 are less than ±5%, which indicates that the calculation of each element of the water cycle in the study area can well reflect the water balance relationship of the basin.

Short-Term and Long-Term Replenishment of Water Storage Influenced by Lockdown and Policy Measures in Drought-Prone Regions of Central India

Central India faces a freshwater shortage due to its diverse terrain, sudden change in precipitation patterns and crystalline rock covered subsurface. Here, we investigate the patterns in terrestrial water storage anomaly (TWSA) over the last two decades, and also study the influence of the COVID-19 lockdown on TWSA in the drought-prone regions of central India, mostly covering the Vidarbha region of the Indian state of Maharashtra. The Vidarbha region is arguably the most drought-affected region in terms of farmer suicides due to crop failure. Our forecast data using multiple statistical approaches show a net TWSA rise in the order of 3.65 to 19.32 km3 in the study area in May 2020. A short-term rise in TWSA in April–May of 2020 is associated with lockdown influenced human activity reduction. A long-term rise in TWSA has been observed in the study region in recent years; the rising TWSA trend is not directly associated with precipitation patterns, rather it may be attributed to the implementation of water management policies.

Topography and climate in the upper Indus Basin: Mapping elevation-snow cover relationships

The Upper Indus Basin (UIB), which covers a wide range of climatic and topographic settings, provides an ideal venue to explore the relationship between climate and topography. While the distribution of snow and glaciers is spatially and temporally heterogeneous, there exist regions with similar elevation-snow relationships. In this work, we construct elevation-binned snow-cover statistics to analyze 3415 watersheds and 7357 glaciers in the UIB region. We group both glaciers and watersheds using a hierarchical clustering approach and find that (1) watershed clusters mirror large-scale moisture transport patterns and (2) are highly dependent on median watershed elevation. (3) Glacier clusters are spatially heterogeneous and are less strongly controlled by elevation, but rather by local topographic parameters that modify solar insolation. Our clustering approach allows us to clearly define self-similar snow-topographic regions. Eastern watersheds in the UIB show a steep snow cover-elevation relationship whereas watersheds in the central and western UIB have moderately sloped relationships, but cluster in distinct groups. We highlight this snow-cover-topographic transition zone and argue that these watersheds have different hydrologic responses than other regions. Our hierarchical clustering approach provides a potential new framework to use in defining climatic zones in the cyrosphere based on empirical data.

Intercomparison of Gridded Precipitation Datasets over a Sub-Region of the Central Himalaya and the Southwestern Tibetan Plateau

Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to overcome the limitations of ground truth observations. They can provide datasets continuous in both space and time. However, there are many products available, which use various methods for data generation and lead to different precipitation values. In our study we compare nine different gridded precipitation products from different origins (ERA5, ERA5-Land, ERA-interim, HAR v2 10 km, HAR v2 2 km, JRA-55, MERRA-2, GPCC and PRETIP) over a subregion of the Central Himalaya and the Southwest Tibetan Plateau, from May to September 2017. Total spatially averaged precipitation over the study period ranged from 411 mm (GPCC) to 781 mm (ERA-Interim) with a mean value of 623 mm and a standard deviation of 132 mm. We found that the gridded products and the few observations, with few exceptions, are consistent among each other regarding precipitation variability and rough amount within the study area. It became obvious that higher grid resolution can resolve extreme precipitation much better, leading to overall lower mean precipitation spatially, but higher extreme precipitation events. We also found that generally high terrain complexity leads to larger differences in the amount of precipitation between products. Due to the considerable differences between products in space and time, we suggest carefully selecting the product used as input for any research application based on the type of application and specific research question. While coarse products such as ERA-Interim or ERA5 that cover long periods but have coarse grid resolution have previously shown to be able to capture long-term trends and help with identifying climate change features, this study suggests that more regional applications, such as glacier mass-balance modeling, require higher spatial resolution, as is reproduced, for example, in HAR v2 10 km.

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle’s acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management.

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

Water Storage Trends in High Mountain Asia

Changes in terrestrial water storage (TWS) in High Mountain Asia (HMA) could have major societal impacts, as the region's large reservoirs of glaciers, snow, and groundwater provide a freshwater source to more than one billion people. We seek to quantify and close the budget of secular changes in TWS over the span of the GRACE satellite mission (2003-2016). To assess the TWS trend budget we consider a new high-resolution mass trend product determined directly from GRACE L1B data, glacier mass balance derived from Digital Elevation Models (DEMs), groundwater variability determined from confined and unconfined well observations, and terrestrial water budget estimates from a suite of land surface model simulations with the NASA Land Information System (LIS). This effort is successful at closing the aggregated TWS trend budget over the entire HMA region, the glaciated portion of HMA, and the Indus and Ganges basins, where the full-region trends are primarily due to the glacier mass balance and groundwater signals. Additionally, we investigate the closure of TWS trends at individual 1-arc-degree mascons (area ≈12,000 km2); a significant improvement in spatial resolution over previous analyses of GRACE-derived trends. This mascon-level analysis reveals locations where the TWS trends are well-explained by the independent datasets, as well as regions where they are not; identifying specific geographic areas where additional data and model improvements are needed. The accurate characterization of total TWS trends and its components presented here is critical to understanding the complex dynamics of the region, and is a necessary step toward projecting future water mass changes in HMA.