Balance Estimates Research Articles

Groundwater Recharge Response to Reduced Irrigation Pumping: Checkbook Irrigation and the Water Savings Payment Plan

Ongoing investments in irrigation technologies highlight the need to accurately estimate the longevity and magnitude of water savings at the watershed level to avoid the paradox of irrigation efficiency. This paradox arises when irrigation pumping exceeds crop water demand, leading to excess water that is not recovered by the watershed. Comprehensive water accounting from farm to watershed scales is challenging due to spatial variability and inadequate socio-hydrological data. We hypothesize that water savings are short term, as prior studies show rapid recharge responses to surface changes. Precise estimation of these time scales and water savings can aid water managers making decisions. In this study, we examined water savings at three 65-hectare sites in Nebraska with diverse soil textures, management practices, and groundwater depths. Surface geophysics effectively identified in-field variability in soil water content and water flux. A one-dimensional model showed an average 80% agreement with chloride mass balance estimates of deep drainage. Our findings indicate that groundwater response times are short and water savings are modest (1–3 years; 50–900 mm over 10 years) following a 120 mm/year reduction in pumping. However, sandy soils with shallow groundwater show minimal potential for water savings, suggesting limited effectiveness of irrigation efficiency programs in such regions.

Accelerated glacier mass loss in the mid-latitude Eurasia from 2019 to 2022 revealed by ICESat-2

The dynamics of glaciers serve as one of the most important indicators of climate change. Whilst current research has primarily concentrated on long-term interannual glacier mass balance and its response to climate change, glaciers may respond more rapidly to climate change, highlighting the urgent need for intra-annual mass balance estimations. Investigating seasonal or short-term variations in glacier mass balance not only enhances our understanding of the interactions between glaciers and the climate system but also provides crucial data for water resource management and ecological protection. The ICESat-2 and NASADEM datasets were used to estimate the inter- and intra-annual glacier mass balance changes in the mid-latitude Eurasia from 2019 to 2022. Additionally, the response of glacier mass balance to regional air temperature and precipitation values was analysed using ERA5-Land data and multiple regression analysis, respectively. From 2019 to 2022, glacier mass loss in mid-latitude Eurasia reached −45.02 ± 34.21 Gt per year, contributing to a global sea-level rise of 0.12 ± 0.09 mm per year. The glacier melt rate in the study area from 2019 to 2022 was 2.33 times higher than that from 2000 to 2019. With the exception of the Western Kunlun region, which experienced a weak accumulation rate of 0.04 ± 0.35 m w.e. per year, all other areas experienced ablation states. Seasonal mass balance responds differently to temperature and precipitation variations across seasons: higher temperatures in different seasons lead to more negative mass balances, while increased winter and spring precipitation can slow down glacier melt. Air temperature dominates the glacier mass balance changes in the study area. The intense heat in 2022 raised average glacier temperatures by 1.04 °C compared to 2019–2021, resulting in a more negative mass balance and an increased ice loss of −0.34 ± 1.01 m w.e. per year (−35.07 ± 103.22 Gt per year). This analysis indicates that glacier mass balance is highly sensitive to climate change, even on a seasonal scale. Moreover, the high precision and spatiotemporal resolution ICESat-2 data can facilitate the investigation of large-scale glacier mass balance on short time scales.

Deriving seasonal and annual surface mass balance for debris-covered glaciers from flow-corrected satellite stereo DEM time series

Abstract Glaciers in High-Mountain Asia are experiencing varying rates and patterns of mass loss due to a complex interplay between glacier surface processes, local conditions and climate forcing. Spatially distributed surface mass balance (SMB) estimates can provide valuable insight into these drivers, but observations are currently limited in both space and time. We used very-high-resolution optical stereo images acquired by commercial satellites to prepare time series of digital elevation models (DEMs), and derived contemporaneous surface velocity and elevation change products for six debris-covered glaciers in Nepal. We developed new methods to produce flow-corrected Lagrangian SMB maps to isolate local surface ablation signals with enough detail to study individual ice cliffs. Our results show reduced ablation under thick debris cover and enhanced ablation over ice cliffs. Ablating ice cliffs were responsible for $10\!-\!38\%$ of the total ablation over debris-covered areas, even though they covered $\leq \!11\%$ of the total area. Seasonal SMB products reveal the timing and patterns of summer accumulation and ablation, underscoring the importance of snow avalanches for low-elevation debris-covered glaciers in the region. Our approach can be applied to other glaciers with repeat high-resolution DEM coverage and extended for regional analyses of SMB on seasonal to interannual timescales.

Tiered maize and wheat nutrient removal coefficients estimated from available data

AbstractEstimates of cropland nutrient budgets at national to global scale generally rely on regional or global mean coefficients for quantifying nutrients removed in crop yield and by-products. Use of such mean values masks the variability in these coefficients. Using maize and wheat as examples, we assessed variation in nutrient removal coefficients, namely harvest index (HI), nitrogen (N), phosphorus (P) and potassium (K) concentrations of crop products (Grain N, Grain P and Grain K respectively) and N, P and K concentrations of crop residues (Residue N, Residue P, and Residue K respectively). Variation in these coefficients was assessed by three categories (Tiers) of estimation. Statistical (mixed-effects) and machine learning (random forest regression) models (Tier 3) were used to predict the coefficients using generally available predictor variables at a global level. Mean prediction accuracies (R2) of the mixed-effects and random forest models were 0.32 for maize coefficients and 0.45 for wheat coefficients when based on a random sub-selection of mainly replicated field experiment data. When predictions were applied to on-farm data only, prediction accuracies were lower (mean R2 values of 0.08 and 0.36 for maize and wheat respectively). Variation in, and dearth of on-farm data for the coefficients contributed to these poor prediction accuracies. Until the limitations of on-farm data are overcome, it is recommended to use Tier 2 (regional) coefficient estimates in country and global cropland nutrient balance and nutrient use efficiency estimates. Where Tier 2 values are not available, then global average (Tier 1) coefficients can be used.

Reconciled estimation of Antarctic ice sheet mass balance and contribution to global sea level change from 1996 to 2021

AbstractThe Antarctic Ice Sheet (AIS) has been losing ice mass and contributing to global sea level rise (GSLR). Given its mass that is enough to cause ∼58 m of GSLR, accurate estimation of mass balance trend is critical for AIS mass loss monitoring and sea level rise forecasting. Here, we present an improved approach to reconciled solutions of mass balance in AIS and its regions from multiple contributing solutions using the input-out, altimetric, and gravimetric methods. In comparison to previous methods, such as IMBIE 2018, this approach utilizes an adaptive data aggregation window to handle the heterogeneity of the contributing solutions, including the number of solutions, temporal distributions, uncertainties, and estimation techniques. We improved the regression-based method by using a two-step procedure that establishes ensembled solutions within each method (input-output, altimetry, or gravimetry) and then estimates the method-independent reconciled solutions. For the first time, 16 contributing solutions from 8 Chinese institutions are used to estimate the reconciled mass balance of AIS and its regions from 1996 to 2021. Our results show that AIS has lost a total ice mass of ∼3213±253 Gt during the period, an equivalent of ∼8.9±0.7 mm of GSLR. There is a sustained mass loss acceleration since 2006, from 88.1±3.6 Gt yr−1 during 1996–2005 to 130.7±8.4 Gt yr−1 during 2006–2013 and further to 157.0±9.0 Gt yr−1 during 2014–2021. The mass loss signal in the West Antarctica and Antarctic Peninsula is dominant and clearly presented in the reconciled estimation and contributing solutions, regardless of estimation methods used and fluctuation of surface mass balance. Uncertainty and challenges remain in mass balance estimation in East Antarctica. This reconciled estimation approach can be extended and applied for improved mass balance estimation in the Greenland Ice Sheet and mountain glacier regions.

First Red List of Ecosystems assessment of a tropical glacier ecosystem to diagnose the pathways towards imminent collapse

Abstract Tropical glaciers are rapidly disappearing, particularly in isolated mountain peaks below 5,000 m elevation. These glaciers are fundamental substrates for unique cryogenic ecosystems in high tropical environments where the ice, melting water and rocky substrate sustain microbiological communities and other meso- and macro-biota. This study uses the Red List of Ecosystems guidelines to diagnose the collapse of the tropical glacier ecosystem of the Cordillera de Mérida, Venezuela. We undertook the assessment with existing estimates of glacier ice extent, indirect historical estimates of ice mass balance and global mechanistic models of future ice mass balance. We complemented these with additional statistical analysis of trends and bioclimatic suitability modelling to calculate and predict rates of decline and relative severity of degradation in selected ecosystem indicators. The evidence suggests an extreme risk of collapse (Critically Endangered) because of a prolonged and acute reduction in ice extent and changes in climatic conditions that are leading to the complete loss of ice mass. The ice substrate has declined 90% in the last 20 years, and observed acceleration of the rate of decline suggests it will probably disappear within the next 5 years. Loss of ice substrate will trigger an immediate loss of supraglacial, englacial and subglacial biotic compartments and initiate a decades-long succession of forefield vegetation. However, ongoing inventories of native biota and monitoring of ecosystem transitions can provide valuable insights and lessons for other ecosystems facing similar risks. The Red List of Ecosystems assessment protocol provides a useful framework for comparative analysis of cryogenic ecosystems.

Expert-based prior uncertainty analysis of gridded water balance components: Application to the irrigated Hindon River Basin, India

Study regionHindon River Basin, North India. Study focusAccurate estimation of water balance components is crucial for water management applications yet challenging due to errors in monthly gridded water balance data products. Error and uncertainty quantification is especially important in the absence of extensive in-situ data. This paper presents a prior uncertainty analysis for such situations consisting of two components: (i) quantification of prior uncertainties using metrics that quantify errors in individual products and variability and consistency between products, and (ii) reduction of prior uncertainties by eliminating unrealistic water balance estimates. New hydrological insights for the regionGrid-scale inter-product uncertainty or variability, computed as the coefficient of variation (CV, %) at various temporal scales, reveals discrepancies between water balance products due to a combination of factors, including methodological differences, inherent spatial variability, data sources, and resolution disparities. At the mean annual scale, P fluxes display a lower grid-scale inter-product uncertainty (5–9 %) than ET (20–55 %), while the ∆TWS from GRACE solutions show a moderate mean annual grid-scale inter-product uncertainty (15–19 %). Grid-scale inter-product uncertainties of ∆SMS for July – representing the onset of the monsoon season – are high (CV = 54–122 %), indicating that the uncertainty in estimates of this component may have a large impact on water balance analyses. P fluxes exhibited fewer spatio-temporal uncertainties (R2 above 0.8) than ET fluxes (R2 less than 0.75). The exclusion of the unreliable data sets resulted in (a) reducing uncertainties in input water balance components with triple collocation range shifting from 15–38 to 17–23 mm/month for ET and from 16–52 to 11–23 mm/month for P, (b) obtaining updated prior estimates of seasonal water balance. The updated priors of water balance variables per season suggest a net basin outflow (from −318 to −57 mm/season) during the monsoon (rainy) season and net basin inflow (from −38 to 330 mm/season) during the non-monsoon (dry) season, the latter related to surface-water imports from outside the basin. All GRACE data sets exhibit a regional long-term decreasing trend in total water storage (ranging from −31 to −61 mm/year), qualitatively confirming previously documented unsustainable groundwater depletion in the basin. Prior ranges and uncertainties for all water balance variables reported here can be used as input into a posterior analysis that uses in-situ data for locally calibrating (bias-correcting, noise-filtering) and further updating the prior estimates.

High-resolution elevation models of Larsen B glaciers extracted from 1960s imagery

Accelerated warming since the 1950s has caused dramatic change to ice shelves and outlet glaciers on the Antarctic Peninsula. Long observational records of ice loss in Antarctica are rare but essential to accurately inform mass balance estimates of glaciers. Here, we use aerial images from 1968 to reveal glacier configurations in the Larsen B region. We use structure-from-motion photogrammetry to construct high-resolution (3.2 m at best) elevation models covering up to 91% of Jorum, Crane, Mapple, Melville and Flask Glaciers. The historical elevation models provide glacier geometries decades before the Larsen B Ice Shelf collapse in 2002, allowing the determination of pre-collapse and post-collapse elevation differences. Results confirm that these five tributary glaciers of the former Larsen B Ice Shelf were relatively stable between 1968 and 2001. However, the net surface elevation differences over grounded ice between 1968 and 2021 equate to 35.3 ± 1.2 Gt of ice loss related to dynamic changes after the ice shelf removal. Archived imagery is an underutilised resource in Antarctica and was crucial here to observe glacier geometry in high-resolution decades before significant changes to ice dynamics.

Isotopic evaluation of the National Water Model reveals missing agricultural irrigation contributions to streamflow across the western United States.

The National Water Model (NWM) provides critical analyses and projections of streamflow that support water management decisions. However, the NWM performs poorly in lower-elevation rivers of the western United States (US). The accuracy of the NWM depends on the fidelity of the model inputs and the representation and calibration of model processes and water sources. To evaluate the NWM performance in the western US, we compared observations of river water isotope ratios ( and expressed in notation) to NWM-flux-estimated (model) river reach isotope ratios. The modeled estimates were calculated from long-term (2000-2019) mean summer (June, July, and August) NWM hydrologic fluxes and gridded isotope ratios using a mass balance approach. The observational dataset comprised 4503 in-stream water isotope observations in 877 reaches across 5 basins. A simple regression between observed and modeled isotope ratios explained 57.9 % ( ) and 67.1 % ( ) of variance, although observations were 0.5 ‰ ( ) and 4.8 ‰ ( ) higher, on average, than mass balance estimates. The unexplained variance suggest that the NWM does not include all relevant water fluxes to rivers. To infer possible missing water fluxes, we evaluated patterns in observation-model differences using ( ) and ( ). We detected evidence of evaporation in observations but not model estimates (negative and positive ) at lower-elevation, higher-stream-order, arid sites. The catchment actual-evaporation-to-precipitation ratio, the fraction of streamflow estimated to be derived from agricultural irrigation, and whether a site was reservoir-affected were all significant predictors of in a linear mixed-effects model, with up to 15.2 % of variance explained by fixed effects. This finding is supported by seasonal patterns, groundwater levels, and isotope ratios, and it suggests the importance of including irrigation return flows to rivers, especially in lower-elevation, higher-stream-order, arid rivers of the western US.

Quantification of Nocturnal Water Use and Its Composition in a Eucalyptus urophylla × E. grandis Plantation on the Leizhou Peninsula, South China

AbstractNocturnal water use (Qnight) is an important component of the eucalyptus water budget, but it has always been under‐appreciated and poorly understood. To improve the accuracy of water balance estimates and the understanding of the nocturnal water use process in eucalypts plantations, we conducted a 3‐year study to investigate the characteristics of Qnight and its components in an E. urophylla × E. grandis plantation in southern China. The results showed that the Qnight of E. urophylla × E. grandis was substantial and the ratio of nocturnal to daily water use (Rnight) was on average 12.35%, with higher Rnight (14.97%) in the dry season than in the wet season (9.50%). The Qnight includes two components, nocturnal transpiration (Tn) and nocturnal refilling (Re), which are driven by different factors. Nocturnal Re‐Tn dynamics were controlled by a combination of nocturnal environmental factors that drive Tn and corresponding daytime environmental factors that drive daytime transpiration. Therefore, the compositional ratios of Tn and Re differed between weather conditions and months. We developed a novel method to distinguish between Re and Tn and quantified the dynamics of their ratios. We found that on a 3‐year average, the Qnight of E. urophylla × E. grandis was mainly used for Tn (58.63%). Our results highlight the non‐ignorability of Qnight and the high variability of the compositional ratios of Re and Tn, and suggest that Qnight and its components should be accurately quantified and considered when studying the water balance in eucalyptus stands.

A novel gradient boosting approach for imbalanced regression

Data imbalance is prevalent in the real world and has received significant attention in classification tasks, both theoretically and practically. However, imbalanced regression remains underexplored. It involves continuous labels, which is laborious and technically difficult due to the absence of distinct boundaries between different targets. To address this issue, we leverage the balanced mean square error (Balanced MSE) from a statistical perspective into the gradient boosting algorithm framework to present the imbalanced regression gradient boosting algorithm (IMr-GB). This algorithm could adapt to imbalanced prior distributions and achieve tradeoffs between frequent and rare labels, thus delivering balanced estimation and effectively reducing adverse effects on underrepresented datasets. In addition, we devise a Bayes variant of IMr-GB, denoted as IMr-bay-GB, to eliminate the complexity of the number of Gaussian mixture components and acquire robustness and optimal performance. Our strategies are extensively tested on ten real-world data sets to demonstrate their superior performance.

Exploring Oman’s International Tourism Determinants in a Gravity Modelling Framework

Abstract This research investigates Oman’s international tourism predictors within a coherent gravity modelling framework. International visitor arrival data for 2007 to 2015 involving Oman’s forty-eight main visitor origin countries are incorporated in the balanced panel estimation empirical phase that tests several gravity model specifications. The estimation procedure includes standard gravity variables and controls for several hypothesized influences. The findings provide strong evidence that the economic sizes of visitor recipient and origin countries, the distance between Oman and visitor origin countries, humidity, quality of accommodation, and common religion are statistically significant predictors. Surprisingly, while relevant, Oman’s cultural attractions (museums, forts, and castles) and the extent of human freedom are statistically insignificant. These findings are unique as they focus on the importance of factors determining Oman’s tourism within a cohesive and inclusive modelling framework. Policymakers can target the abovementioned variables to facilitate investments and propel tourism.

Distributed state-of-charge and power balance estimation for aggregated battery energy storage systems with EV aggregators

Aggregated battery energy storage systems (ABESSs) play an important role in smart grids. This study considers distributed ABESSs containing electric vehicle (EV) aggregators and battery energy storage systems (BESSs). Due to randomness and uncertainty caused by EV aggregators, the distributed ABESSs face heterogeneous random communication failures between the EV aggregators and the BESSs. Aiming to solve this problem, this study proposes a distributed state-of-charge (SoC) and power balance estimation strategy for ABESSs with an event-triggered mechanism. Different from the traditional SoC balance problem definition in the BESSs, this study introduces an innovative balance convergence condition to solve the problem of power imbalance between the EV aggregators and the BESS units. In addition, an adaptively robust estimation algorithm is designed to estimate the precise value of SoC and power under heterogeneous random failures caused by EV aggregators. Further, a distributed periodic dynamic event-triggered mechanism is developed considering redundant messages in the ABESS communication channels. This mechanism can both handle the Zeno phenomenon and obtain a larger minimum time interval with fewer sent measurements. The simulation results demonstrate the effectiveness of the proposed distributed approach in both the discharging and charging modes of ABESSs.

Estimating digital product trade through corporate revenue data

Despite global efforts to harmonize international trade statistics, our understanding of digital trade and its implications remains limited. Here, we introduce a method to estimate bilateral exports and imports for dozens of sectors starting from the corporate revenue data of large digital firms. This method allows us to provide estimates for digitally ordered and delivered trade involving digital goods (e.g. video games), productized services (e.g. digital advertising), and digital intermediation fees (e.g. hotel rental), which together we call digital products. We use these estimates to study five key aspects of digital trade. We find that, compared to trade in physical goods, digital product exports are more spatially concentrated, have been growing faster, and can offset trade balance estimates, like the United States trade deficit on physical goods. We also find that countries that have decoupled economic growth from greenhouse gas emissions tend to have larger digital exports and that digital exports contribute positively to the complexity of economies. This method, dataset, and findings provide a new lens to understand the impact of international trade in digital products.

Estimation of Energy Balance throughout the Growing–Finishing Stage of Pigs in an Experimental Pig Barn

Monitoring the energy inputs and outputs in pig production systems is crucial for identifying potential imbalances and promoting energy efficiency. Therefore, the objective of this study was to measure the energy input, output, and losses during the growing–finishing phase of pigs from 1 September to 1 December 2023. A Livestock Environment Management System (LEMS) was used to measure the temperature, humidity, airflow, and water consumption levels inside the barn, and a load cell was used to measure the body weight of pigs. Furthermore, a bomb calorimetric test was conducted to measure the energy content of pigs’ manure. While calculating energy balance in the experimental barn, it was found that energy from feed and water contributed approximately 81% of the total input energy, while the remaining 19% of energy came from electrical energy. Regarding output energy, manure, and body weight accounted for about 69%, while around 31% was lost due to pig activities, maintaining barn temperature and airflow, and illuminating the barn. In conclusion, this study suggested methods to calculate energy balance in pig barns, offering valuable insights for pig farmers to enhance their understanding of input and output energy in pig production.

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction.

Wire-arc additive manufacturing (WAAM) is favored by the industry for its high material utilization rate and low cost. However, wire-arc additive manufacturing of lattice structures faces problems with forming accuracy such as broken rod and surface morphology defects, which cannot meet the industrial demand. This article innovatively combines the melt pool stress theory with visual perception algorithms to visually study the force balance of the near-suspended melt pool to predict the state of the melt pool. First, the method for melt pool segmentation was studied. The results show that the optimized U-net achieved high accuracy in melt pool segmentation tasks, with accuracies of 98.18%, MIOU 96.64%, and Recall 98.34%. In addition, a method for estimating melt pool force balance and predicting normal, sagging, and collapsing states of the melt pool is proposed. By combining experimental testing with computer vision technology, an analysis of the force balance of the melt pool during the inclined rod forming process was conducted, showing a prediction rate as high as 90% for the testing set. By using this method, monitoring and predicting the state of the melt pool is achieved, preemptively avoiding issues of broken rods during the printing process. This approach can effectively assist in adjusting process parameters and improving welding quality. The application of this method will further promote the development of intelligent unmanned WAAM and provide some references for the development of artificial intelligence monitoring systems in the manufacturing field.

Temperature variations impacting leaf senescence initiation pathways alter leaf fall timing patterns in northern deciduous forests

Simulating the timing of leaf fall in large scale is crucial for accurate estimation of ecosystem carbon sequestration. However, the limited understanding of leaf senescence mechanisms often impedes the accuracy of simulation and prediction. In this study, we employed the advanced process-based models to fit remote sensing-derived end dates of the growing season (EOS) across deciduous broadleaf forests in the Northern Hemisphere, and revealed the spatial pattern associated with two leaf senescence pathways (i.e., either photoperiod- or temperature- initiated leaf senescence) and their potential effects on EOS prediction. The results show that the pixel-specific optimum models effectively fitted all EOS time series. Leaf senescence in 67.6 % and 32.4 % of pixels was initiated by shortening daylength and declining temperature, respectively. Shortening daylength triggered leaf senescence occurs mainly in areas with shorter summer daylength and/or warmer autumns, whereas declining temperature induced leaf senescence appears primarily in areas with longer summer daylength and/or colder autumns. The strong dependence of leaf senescence initiation cues on local temperature conditions implies that the ongoing increase in autumn temperature has the potential to alter the leaf senescence initiation, shifting from temperature cues to photoperiod signals. This shift would occur in 26.2–49.6 % of the areas where leaf senescence is initiated by declining temperature under RCP 4.5 and 8.5 scenarios, while forest areas where leaf senescence is induced by shortening daylength may expand northward. The overall delaying of the currently predicted EOS would therefore slow down by 4.5–10.3 % under the two warming scenarios. This implies that the adaptive nature of plants will reduce the overestimation of changes in carbon exchange capacity between ecosystems and atmosphere. Our study offers novel insights into understanding the mechanism of leaf senescence and improving the estimation of autumn phenology and ecosystem carbon balance in the deciduous broadleaf forests.

Wetland water balance estimation in an arid region using remote sensing technology and hydrological modelling

ABSTRACT Understanding the hydrological processes associated with wetland dynamics is fundamental to studying climate change impacts and the global water cycle. This study simulates the variation of water balance in the Ghorra Playa, Eastern Tunisia, over the period extending from September 2017 to August 2020. The playa is a seasonal habitat for a number of migratory bird species and its watershed is of vital agricultural importance. Water balance estimations were performed using two different approaches: remote sensing-based analyses (Sentinel-2B, Global Precipitation Measurements (GPM) and Famine Early Warning System Network (FEWS-NET)) and modelling founded on field data. The playa presents an average annual water balance of 1.1 million cubic metres. Water inflows come from direct rainfall, mostly in the fall and spring seasons. Groundwater flow into the playa significantly influenced the pattern of water flux (81%). The annual, seasonal and monthly water budget simulations show reasonably good agreement between the remote sensing-based analysis and the hydrological modelling exercise.

Estimating water balance in a Brazilian semiarid watershed using different spatial data

This study advances water balance (WB) estimation in the Paraguaçu River watershed, Brazil, by leveraging spatial data to mitigate the challenges of sparse monitoring, particularly in the semiarid region. By combining precipitation (PPT) and actual evapotranspiration (ETR) data from diverse methodologies, including IMERG, TMPA, TerraClimate (TC), and IDW interpolation, alongside ETR sources like MOD16, GLEAM, and TerraClimate, we aim to capture the watershed's physical and climatic nuances. The analysis reveals that TerraClimate data (PPTTC and ETRTC) most accurately reflect spatial variations, effectively capturing orographic effects and the semiarid climate, thus emerging as the optimal combination for depicting WB (WBTC_TC). Key findings highlight the variable precision of PPT and ETR data sources in characterizing spatial distribution and magnitude across the watershed, with TerraClimate data showing superior sensitivity to regional disparities. This sensitivity is crucial for accurately modeling the hydrological dynamics in regions with contrasting climatic conditions, from humid orographic areas to the semiarid zones. Moreover, the study underscores the importance of data validation and careful integration of spatial data, particularly in under-monitored or climatically extreme regions, to enhance the reliability of hydrological analyses. The successful integration of spatial data underscores its value in remote or data-scarce regions, reinforcing the necessity for rigorous spatial and temporal validation. This approach not only improves water management strategies but also enriches the integration with climatic and hydrological models, offering a comprehensive toolkit for addressing the complexities of watershed management in the face of climate variability.

A 2012–2021 high‐resolution glacier mass balance estimate for Icelandic ice caps based on ArcticDEM and ICESat‐2

AbstractIce caps are important water resources for Iceland and are threatened by climate change. Despite their importance and vulnerability, the geodetic glacier mass balance estimate for the recent decade lacks a high‐resolution result. To address this gap, we first co‐registered the 2012–2021 ArcticDEM (v4) strips with ICESat‐2 points acquired over off‐ice areas and evaluated the accuracy of the co‐registered DEMs. We then applied a per‐pixel weighted liner regression to the multi‐temporal ArcticDEM strips and generated a high‐resolution (2 m) glacier surface elevation change rate for six major Icelandic ice caps. Based on these estimates we calculated the geodetic mass balance. Additionally, we estimated the seasonal relative surface elevation changes using ICESat‐2 ATL06 data for the 2019 to 2021 period. The results showed the co‐registered ArcticDEM strips exhibit good accuracy, with a standard deviation of 1.15 m across the Icelandic ice cap regions. Between 2012 and 2021, Icelandic ice caps experienced a state of mass loss, with a surface elevation change rate of −0.57 ± 0.21 m/a, corresponding to a mass loss of −0.51 ± 0.02 m w.e/a. Spatial variability in glacier surface elevation changes among different Icelandic ice caps ranges −0.19 m/a to −0.75 m/a. The Langjökull ice cap exhibited the most severe glacier mass loss at −0.68 ± 0.006 m w.e/a. Furthermore, our observations indicate a change in seasonal trends occurred across all six ice caps from 2019 to 2021. Specifically, the glacier surface thickening decreased during the accumulation period, while the thinning rate increased during the ablation period. These findings highlight that ArcticDEM v4 strips provide new insights into the mass balance of Icelandic glaciers and will aid in making future water management decisions. Moreover, the high‐resolution surface elevation topography and change rates provided in this study may contribute to an improved understanding of the influence of glacier dynamics on glacier change.