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Water resources in Central Asia strongly depend on glaciers, which in turn adjust their size in response to climate variations. We investigate glacier runoff in the period 1981–2019 in the upper Naryn basin, Kyrgyzstan. The basins contain more than 1,000 glaciers, which cover a total area of 776 km2. We model the mass balance and runoff contribution of all glaciers with a simplified energy balance melt model and distributed accumulation model driven by ERA5 LAND re-analysis data for the time period of 1981–2019. The results are evaluated against discharge records, satellite-derived snow cover, stake readings from individual glaciers, and geodetic mass balances. Modelled glacier volume decreased by approximately 6.7 km3 or 14%, and the majority of the mass loss took place from 1996 until 2019. The decreasing trend is the result of increasingly negative summer mass balances whereas winter mass balances show no substantial trend. Analysis of the discharge data suggests an increasing runoff for the past two decades, which is, however only partly reflected in an increase of glacier melt. Moreover, the strongest increase in discharge is observed in winter, suggesting either a prolonged melting period and/or increased groundwater discharge. The average runoff from the glacierized areas in summer months (June to August) constitutes approximately 23% of the total contributions to the basin’s runoff. The results highlight the strong regional variability in glacier-climate interactions in Central Asia.

Background. Annually, around 121 million unintended pregnancies occur in the world and more than 73 million encountered abortion. Ethiopia is also losing 19.6% of mothers due to unsafe abortion. Despite that postabortion contraceptive service is a climactic entry point for the prevention of unwanted pregnancy and associated deaths, the service magnitude and determinants immediately before discharge are not characterized well in Ethiopia. Hence, this study aimed to assess the magnitude of postabortion contraceptive utilization and associated factors among women receiving abortion care service before being discharged from health facilities in Harar, Eastern Ethiopia. Methods. A facility-based cross-sectional study was conducted among 390 women receiving abortion care services. At discharge, data about contraceptive acceptance and related maternal characteristics were collected. A binary logistic regression model was used to assess the association between independent and dependent variables (postabortion contraceptive utilization). Analysis was done with SPSS 22. Statistical significance was considered at P < 0.05 . Result. The overall prevalence of postabortion contraceptive utilization was 81.5% (95% CI: 77.9, 85.4). Being unmarried (AOR, 0.05; 95% CI (0.02, 0.16)), having no history of previous abortion (AOR, 0.11; 95% CI (0.04, 0.34)), being multigravida (AOR 8.1; 95% CI (2.20, 13.40), lacking desire to have an additional child (AOR, 6.3; 95% CI (2.65, 15.34), and history of family planning use (AOR, 17.20; 95% CI (6.5, 38.60)) were determinants of postabortion contraceptive utilization before being discharged from the health facilities. Conclusion. Postabortion contraceptive utilization in Harar health facilities still needs improvement as per the WHO and national recommendations. Therefore, the family planning provision strategies should be convincing and friendly, especially for unmarried mothers, and those who had no history of abortion should be counseled in friendly and systematically convincing schemes for enabling them to take the service before discharge from the health facility.

A significant constraint in water resource development in developing countries is the lack of accurate river discharge data. Stage–discharge measurements are infrequent, and rating curves are not updated after major storms. Therefore, the objective is to develop accurate stage–discharge rating curves with limited measurements. The Lake Tana basin in the upper reaches of the Blue Nile in the Ethiopian Highlands is typical for the lack of reliable streamflow data in Africa. On average, one stage–discharge measurement per year is available for the 21 gaging stations over 60 years or less. To obtain accurate and unique stage–discharge curves, the discharge was expressed as a function of the water level and a time-dependent offset from zero. The offset was expressed as polynomial functions of time (up to order 4). The rating curve constants and the coefficients for the polynomial were found by minimizing the errors between observed and predicted fluxes for the available stage–discharge data. It resulted in unique rating curves with R2 > 0.85 for the four main rivers. One of the river bottoms of the alluvial channels increased in height by up to 3 m in 60 years. In the upland channels, most offsets changed by less than 50 cm. The unique rating curves that account for temporal riverbed changes can aid civil engineers in the design of reservoirs, water managers in improving reservoir management, programmers in calibration and validation of hydrology models and scientists in ecological research.

Missing observational data pose an unavoidable problem in the hydrological field. Deep learning technology has recently been developing rapidly, and has started to be applied in the hydrological field. Being one of the network architectures used in deep learning, Long Short-Term Memory (LSTM) has been applied largely in related research, such as flood forecasting and discharge prediction, and the performance of an LSTM model has been compared with other deep learning models. Although the tuning of hyperparameters, which influences the performance of an LSTM model, is necessary, no sufficient knowledge has been obtained. In this study, we tuned the hyperparameters of an LSTM model to investigate the influence on the model performance, and tried to obtain a more suitable hyperparameter combination for the imputation of missing discharge data of the Daihachiga River. A traditional method, linear regression with an accuracy of 0.903 in Nash–Sutcliffe Efficiency (NSE), was chosen as the comparison target of the accuracy. The results of most of the trainings that used the discharge data of both neighboring and estimation points had better accuracy than the regression. Imputation of 7 days of the missing period had a minimum value of 0.904 in NSE, and 1 day of the missing period had a lower quartile of 0.922 in NSE. Dropout value indicated a negative correlation with the accuracy. Setting dropout as 0 had the best accuracy, 0.917 in the lower quartile of NSE. When the missing period was 1 day and the number of hidden layers were more than 100, all the compared results had an accuracy of 0.907–0.959 in NSE. Consequently, the case, which used discharge data with backtracked time considering the missing period of 1 day and 7 days and discharge data of adjacent points as input data, indicated better accuracy than other input data combinations. Moreover, the following information is obtained for this LSTM model: 100 hidden layers are better, and dropout and recurrent dropout levels equaling 0 are also better. The obtained optimal combination of hyperparameters exceeded the accuracy of the traditional method of regression analysis.

Abstract Karst groundwater resources in the Zagros Mountains are vital for supplying of different demands in the region which need to sustainable management and protection. Quantitative and qualitative characterization of karst aquifers in this region were understudied due to lack of site-specific logging-data and speleological investigations. In this study, a state-of-the-art of the statistical methods developed to characterize karst aquifer based on analyses of the spring recession hydrograph and spring water quality are presented. These methods including Manging’s method for classification of karst aquifers, relationships of precipitation and discharge data, groundwater quality index (GQI), hydrochemical diagrams (Piper, Durov and Gibbs), and Saturation index (SI), Chloro-Alkaline indices (CAI). 42 major karst springs mainly located in folded part of Zagros region (western Iran) are selected for application of the reviewed methods. Results indicated that the saturated zone exerts almost main control over the discharge of 76% of the studied springs. The base-flow contributes as between 80.0% to 100% of total water storage in the study aquifers. 78.5% of the studied aquifers have a high karstification degree. An insignificant lag-time is observed between the precipitation on the karst basin and spring discharge. The hydrochemical diagrams show that the waters are dominated by HCO3 and Ca and the majority of the waters are alkaline, with originate from silicate minerals weathering. Such repeatable methods adopted in this study can provide crucial information of the karst aquifers, especially those suffer scarcity of aquifer hydrodynamic data.

The glaciers of the North Cascades have experienced mass loss and terminus retreat due to climate change. The meltwater from these glaciers provides a flux of cold glacier meltwater into the river systems, which supports salmon spawning during the late summer dry season. The Nooksack Indian Tribe monitors the outlet flow of the Sholes Glacier within the North Cascades range with the goal of understanding the health of the glacier and the ability of the Tribe to continue to harvest sustainable populations of salmon. This study compares the UAV derived glacier ablation with the discharge data collected by the Tribe. We surveyed the Sholes Glacier twice throughout the 2020 melt season and, using Structure-from-Motion technology, generated high resolution multispectral orthomosaics and Digital Elevation Models (DEMs) of the glacier on each of the survey dates. The DEMs were differenced to reveal the surface height change of the glacier. The spectral data of the orthomosaics were used to conduct IsoData unsupervised classification. This process divided the survey area into Snow, Ice, and Rock classes that were then used to attribute the surface height changes of the DEMs to either snow or ice melt. The analysis revealed the glacier lost an average thickness of −0.132 m per day (m d−1) with snow and ice losing thickness at similar rates, −0.130 m d−1 and −0.132 m d−1 respectively. DEM differencing reveals that a total of −550,161 ± 45,206 m3 water equivalent (w.e.) was discharged into Wells Creek between the survey dates whereas the stream gauge station measured a total discharge of 350,023 m3. This study demonstrates the ability to spectrally classify the UAV data and derive discharge measurements while evaluating the small-scale spatial variability of glacier melt. Assessing ablation in small alpine glaciers is of great importance to downstream communities, like the Nooksack Indian Tribe who seek to understand the magnitude and timing of glacier melt in order to better protect their salmon populations. With this paper, we provide a baseline for future glacier monitoring and the potential to connect the snow surface properties with the rate of snow melt into a warming future.

Various sampling strategies come into operation to monitor water quality in rivers. Most frequently, grab samples are taken, but they are not suitable for recording the highly dynamic transport of solids and solid-bound pollutants. Composite samples reduce the influence of input and transport dynamics and are better suited to determine the annual river loads. Large-volume samplers (LVSs) produce both a composite sample over a long period of time and an amount of solids which allows for further analyses. In the small sub-catchment area of the Kraichbach river in Baden-Wuerttemberg (Germany) two LVSs have been installed to sample the river flow. The concentration of solids and phosphorus in the supernatant water and the settled sediment in the sampler have been determined and mean concentrations have been derived. Annual river loads were calculated in combination with discharge data from the nearby gauging station. Two sampling strategies of the LVS were tested and compared. For the first strategy, the LVS was used to collect quasi-continuous composite samples throughout the year, whereas, with the second strategy, samples were taken specifically for different flow conditions (low, mean and high flow). This study compares the advantages and constraints of both strategies. Results indicate that the first strategy is better suited to determine annual river loads. Quasi-continuous large-volume composite sampling is recommended for further monitoring campaigns.

Abstract Background Most epidemiologic reports focus on lower extremity amputation (LEA) caused specifically by diabetes mellitus. However, narrowing scope disregards the impact of other causes and types of limb amputation (LA) diminishing the true incidence and societal burden. We explored the rates of LEA and upper extremity amputation (UEA) by level of amputation, sex and age over 14 years in Saskatchewan, Canada. Methods We calculated the differential impact of amputation type (LEA or UEA) and level (major or minor) of LA using retrospective linked hospital discharge data and demographic characteristics of all LA performed in Saskatchewan and resident population between 2006 and 2019. Rates were calculated from total yearly cases per yearly Saskatchewan resident population. Joinpoint regression was employed to quantify annual percentage change (APC) and average annual percent change (AAPC). Negative binomial regression was performed to determine if LA rates differed over time based on sex and age. Results Incidence of LEA (31.86 ± 2.85 per 100,000) predominated over UEA (5.84 ± 0.49 per 100,000) over the 14-year study period. The overall LEA rate did not change over the study period (AAPC -0.5 [95% CI − 3.8 to 3.0]) but fluctuations were identified. From 2008 to 2017 LEA rates increased (APC 3.15 [95% CI 1.1 to 5.2]) countered by two statistically insignificant periods of decline (2006–2008 and 2017–2019). From 2006 to 2019 the rate of minor LEA steadily increased (AAPC 3.9 [95% CI 2.4 to 5.4]) while major LEA decreased (AAPC -0.6 [95% CI − 2.1 to 5.4]). Fluctuations in the overall LEA rate nearly corresponded with fluctuations in major LEA with one period of rising rates from 2010 to 2017 (APC 4.2 [95% CI 0.9 to 7.6]) countered by two periods of decline 2006–2010 (APC -11.14 [95% CI − 16.4 to − 5.6]) and 2017–2019 (APC -19.49 [95% CI − 33.5 to − 2.5]). Overall UEA and minor UEA rates remained stable from 2006 to 2019 with too few major UEA performed for in-depth analysis. Males were twice as likely to undergo LA than females (RR = 2.2 [95% CI 1.99–2.51]) with no change in rate over the study period. Persons aged 50–74 years and 75+ years were respectively 5.9 (RR = 5.92 [95% Cl 5.39–6.51]) and 10.6 (RR = 10.58 [95% Cl 9.26–12.08]) times more likely to undergo LA than those aged 0–49 years. LA rate increased with increasing age over the study period. Conclusion The rise in the rate of minor LEA with simultaneous decline in the rate of major LEA concomitant with the rise in age of patients experiencing LA may reflect a paradigm shift in the management of diseases that lead to LEA. Further, this shift may alter demand for orthotic versus prosthetic intervention. A more granular look into the data is warranted to determine if performing minor LA diminishes the need for major LA.

To understand the runoff-sediment discharge relationship , this study examined the annual runoff and sediment discharge data obtained from the Tangnaihai hydrometric station. The data were decomposed into multiple time scales through Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN). Furthermore, double cumulative curves were plotted and the cointegration theory was employed to analyze the microscopic and macroscopic multi-temporal correlations between the runoff and the sediment discharge and their detailed evolution.