The impact of hydroclimate-driven periodic runoff on hydropower production and management

This study evaluates the impact of hydroclimate-driven periodic runoff on hydropower operations and production, with a focus on how the forecasted biennial periodicity of runoff time series could affect the efficiency of hydropower generation. Hydrologic stochastic processes are utilized to forecast long-term runoff, and seven hydroclimate scenarios are developed to be input into a production management model, allowing for an analysis of how periodic hydroclimate variations influence hydropower management and output. The results reveal that the biennial alternation between wet and dry years is a key factor affecting hydropower operations in the Dalälven River Basin. Notable differences between wet- and dry-year scenarios were observed in terms of power efficiency, production output, and forecasting accuracy. Operating hydropower systems based on dry-year runoff forecasts in wet years results in a 1.63% decrease in production efficiency and a reduction of 9,104 MWh in power generation. Conversely, applying wet-year forecasts in dry years slightly boosts production efficiency by 0.31% and increases power generation by 7,832 MWh. Scenarios that adhere to biennial periodicity offer the highest forecasting accuracy, particularly when applying dry-year forecasts in dry years in winter and spring, which produce the most precise predictions. In contrast, using dry-year forecasts in wet years results in the lowest forecasting accuracy.

Bridging the gap between GRACE and GRACE Follow-On by combining high-low satellite-to-satellite tracking data and satellite laser ranging

The satellite missions GRACE and GRACE Follow-On have undoubtedly been the most important sources to observe mass transport on global scales. Within the Combination Service for Time-Variable Gravity Fields (COST-G), gravity field solutions from various processing centers are being combined to improve the signal-to-noise ratio and further increase the spatial resolution. The time series of monthly gravity field solutions suffer from a data gap of about one year between the two missions GRACE and GRACE Follow-On among several smaller data gaps. We present an intermediate technique bridging the gap between the two missions allowing (1) for a continued and uninterrupted time series of mass observations and (2) to compare, cross-validate and link the two time series. We focus on the combination of high-low satellite-to-satellite tracking (HL-SST) of low-Earth orbiting satellites by GPS in combination with satellite laser ranging (SLR), where SLR contributes to the very low degrees and HL-SST is able to provide the higher spatial resolution at an lower overall precision compared to GRACE-like solutions. We present a complete series covering the period from 2003 to 2022 filling the gaps of GRACE and between the missions. The achieved spatial resolution is approximately 700 km at a monthly temporal resolutions throughout the time period of interest. For the purpose of demonstrating possible applications, we estimate the low degree glacial isostatic adjustment signal in Fennoscandia and North America. In both cases, the location, the signal strength and extend of the signal coincide well with GRACE/GRACE-FO solutions achieving 99.5% and 86.5% correlation, respectively.

Monitoring the spatiotemporal terrestrial water storage changes in the Yarlung Zangbo River Basin by applying the P-LSA and EOF methods to GRACE data

The Yarlung Zangbo River Basin is a regulator of water vapor changes in China and even Asia. To avoid the shortcomings of traditional water resource monitoring methods, this study used Gravity Recovery and Climate Experiment (GRACE) data to monitor the terrestrial water storage anomaly (TWSA) in this river from 2002 to 2015 with the help of the polynomial-least squares approach (P-LSA) and the empirical orthogonal function (EOF). The obtained TWSA was compared with hydrometeorological data from several sources to discuss the applicability, uniqueness and response relationship. The results showed that (1) the combination of P-LSA and EOF had strong applicability to explore the TWSA in the study area, with R² = 0.75 and 0.80, respectively, and could indirectly reflect dry and wet conditions in southwestern China. (2) The TWSA revealed significant cyclical and seasonal fluctuations of approximately 12 months and increased from upstream to downstream and from north to south, which was discussed for the first time in the research area. (3) The EOF method can effectively identify the TWSA principal component and structure (EOF1 contribution = 91.08%) by removing noise and redundancy, which is beneficial for revealing the laws essential for TWSA changes. (4) The TWSA in the studied watershed was unique (i.e., the clearest periodic changes with the best fitting effect (R = 0.90); peak, low and peak-low difference values that were 1.82, 1.19 and 1.52 times larger than those of the 8 other rivers; and the largest downward trend of 4.13 mm·a^-1). (5) Rainfall was the decisive factor influencing the TWSA, with correlation coefficients (R) >0.60. This study enhances our overall understanding of the TWSA in this plateau watershed and provides a scientific basis for optimal water resource management.

Remote Sensing of River Discharge: A Review and a Framing for the Discipline

Remote sensing of river discharge (RSQ) is a burgeoning field rife with innovation. This innovation has resulted in a highly non-cohesive subfield of hydrology advancing at a rapid pace, and as a result misconceptions, mis-citations, and confusion are apparent among authors, readers, editors, and reviewers. While the intellectually diverse subfield of RSQ practitioners can parse this confusion, the broader hydrology community views RSQ as a monolith and such confusion can be damaging. RSQ has not been comprehensively summarized over the past decade, and we believe that a summary of the recent literature has a potential to provide clarity to practitioners and general hydrologists alike. Therefore, we here summarize a broad swath of the literature, and find after our reading that the most appropriate way to summarize this literature is first by application area (into methods appropriate for gauged, semi-gauged, regionally gauged, politically ungauged, and totally ungauged basins) and next by methodology. We do not find categorizing by sensor useful, and everything from un-crewed aerial vehicles (UAVs) to satellites are considered here. Perhaps the most cogent theme to emerge from our reading is the need for context. All RSQ is employed in the service of furthering hydrologic understanding, and we argue that nearly all RSQ is useful in this pursuit provided it is properly contextualized. We argue that if authors place each new work into the correct application context, much confusion can be avoided, and we suggest a framework for such context here. Specifically, we define which RSQ techniques are and are not appropriate for ungauged basins, and further define what it means to be ‘ungauged’ in the context of RSQ. We also include political and economic realities of RSQ, as the objective of the field is sometimes to provide data purposefully cloistered by specific political decisions. This framing can enable RSQ to respond to hydrology at large with confidence and cohesion even in the face of methodological and application diversity evident within the literature. Finally, we embrace the intellectual diversity of RSQ and suggest the field is best served by a continuation of methodological proliferation rather than by a move toward orthodoxy and standardization.

Seasonal and Interannual Variations in China’s Groundwater Based on GRACE Data and Multisource Hydrological Models

In this study, we used in situ measurements for the first time to analyze the applicability and effectiveness of evaluating groundwater storage (GWS) changes across China using Gravity Recovery and Climate Experiment (GRACE) satellite products and hydrological data derived from the WaterGap Global Hydrological Model (WGHM), Global Land Data Assimilation System (GLDAS) and eartH2Observe (E2O). The results show that the GWS derived from GRACE JPL Mascons products combined with GLDAS Noah V2.1 data most accurately reflect the overall distribution of GWS changes in China and the correlation coefficient between the in situ measurements reaches 0.538. The empirical orthogonal function decomposition for GWS indicates clear interannual variation and seasonal variation in China. The trends of China’s GWS changes showed a clear regional characteristic from 2003 to 2016. The GWS in the northeast, central-south, and western junction of Xinjiang-Qinghai-Tibet had increased significantly, and the North China Plain (NCP) had a severe decline. The correlation coefficient between the annual trends of precipitation and GWS was 0.57, and it reached 0.73 when four provinces (Beijing, Tianjin, Shanxi, Hebei) that are wholly or partially located in the NCP were excluded. The seasonal variability of GWS in China was obvious and the volatilities in Jiangxi, Hunan and Fujian provinces were the highest, reaching 6.39 cm, 6.33 cm and 5.20 cm, respectively. The empirical orthogonal function decomposition for GWS and precipitation over China indicated seasonal consistency with a correlation coefficient of 0.76. The awareness of areas with significant depletion and large seasonal fluctuation of GWS help adaptations to manage local GWS situation.

Monitoring the spatio-temporal changes of terrestrial water storage using GRACE data in the Tarim River basin between 2002 and 2015

With the threat of water shortages intensifying, the need to identify the terrestrial water storage (TWS) variation in the Tarim River Basin (TRB) becomes very significant for managing its water resource. Due to the lack of large-scale hydrological data, this study employed the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) to monitor TWS variation in the TRB during the period of 2002-2015, cooperating with two statistical techniques, Principal Component Analysis (PCA) - Empirical Orthogonal Function (EOF) and Multiple Linear Regression (MLR). Results indicated that (1) the Tropical rainfall measuring mission (TRMM) data can be applied well in the TRB; (2) the EOF result showed that both the time series of TRMM precipitation and GRACE-derived TWS in the TRB between 2002 and 2015 were dominated by the annual signals, which were followed by the semiannual signals; (3) the linear trend for the spatially averaged GRACE-derived TWS changes exhibited an decrease of 1.6±1.1mm/a, and the EOF result indicated a significant decrease of 4.1±1.5mm/a in the north of TRB; (4) while the precipitation variations was the major driver for the TWS changes, the GLDAS-derived TWS (i.e., soil moisture) decrease and ground water decrease played the major role in the TWS decrease in the north of TRB for the significant correlation (P<0.05). The changes of TWS might be linked to excessive exploitation of water resources, increased population, and shrinking water supplies, which would impact on the water level of the lakes or reservoir.