Simulation of runoff in Baitarani basin using composite and distributed curve number approaches in HEC-HMS model

The present study was conducted in Baitarani basin up to Anandapur gauging station of Odisha covering an area of 8603.7 km2. Pre-processing of basin from digital elevation model (DEM) was done using HEC-Geo-HMS extension and spatial analyst tool in ArcGIS. These pre-processed files were then imported to HEC-HMS for simulating runoff. In this study, runoff simulation was done using two methods, viz., composite and distributed curve number (CN) approaches. SCS curve number method was used for computation of runoff volume, SCS UH method for direct runoff, constant- monthly varying base flow method for base flow and Muskingum method for flow routing. The model was calibrated and validated using both composite and distributed CN approaches. Data from 1st January, 2007 to 31st December, 2013 were used for calibration and 1st January, 2014 to 31st December, 2016 were used for validation. During the calibration period of composite CN approach, the statistical parameters like Nash-Sutcliffe efficiency (NSE), Coefficient of determination (R2), Percent bias (PBIAS) and RMSE-observations standard deviation ratio (RSR) were found to be 0.51, 0.63, 12.82 and 0.7, respectively and during the validation period they were found to be 0.53, 0.54, -19.73 and 0.7, respectively. In case of distributed CN approach, the statistical parameters like NSE, R2, PBIAS and RSR were found to be 0.62, 0.63, -8.64 and 0.6, respectively during the calibration period and 0.67, 0.66, -2.25 and 0.6, respectively during the validation period. The study indicated that distributed CN approach is more accurate than composite CN approach in simulation of runoff using HEC-HMS model.

Influence of calibration period length on predictions of evaporation

<p>Bare soil evaporation is a key component of the soil water balance. Accurate estimation of evaporation is thus critical for sustainable water resources management, especially in arid and semi-arid regions. Numerical models are widely used for estimating bare soil evaporation. Although models allow exploring evaporation dynamics under different hydrological and climatic conditions, their robustness is linked to the reliability of the imposed parameters. These parameters are typically obtained through model calibration. Even if a perfect match between observed and simulated variables is obtained, the predictions are not necessarily reliable. This can be related to model structural errors, or because the inverse problem is ill-posed. While this is conceptually very well known, it remains unclear how the temporal resolution and length of the employed observations for the calibration influence the reliability of the parameters and the predictions.</p><p>We used data from a lysimeter experiment in the Guanzhong Basin, China to systematically explore the influence of the calibration period length on the calibrated parameters and uncertainty of evaporation predictions. Soil water content dynamics and water level were monitored every 5 minutes. We set up twelve models using the fully coupled, physically-based HydroGeoSphere model with different calibration period lengths (one month, three months, six months, fourteen months). The estimated evaporation rates by the models for the calibration period and validation period were compared with the measured evaporation rates. Also, we predict cumulative, one-year evaporation rates. The uncertainty of the predictive evaporation by these models from different calibration lengths is quantified. Several key conclusions can be drawn as follows: (1) The extinction depth is a very important parameter for the soil water content dynamics in the vadose zone but is poorly informed unless the calibration includes significantly different depths to groundwater. (2) Using the longer calibration period length (six months or fourteen months) did not necessarily result in more reliable predictions of evaporation rates. (3) Preliminary results indicate that the uncertainty can be reduced if the calibration period includes both climatic forcing similar to the prediction, but additionally also feature similar water table conditions during calibration and prediction. Our results have implications for reducing uncertainty using unsaturated-saturated models to predict evaporation.</p>

Sampling density and date along with species selection influence spatial representation of tree-ring reconstructions

Our understanding of the natural variability of hydroclimate before the instrumental period (ca. 1900 CE in the United States) is largely dependent on tree-ring-based reconstructions. Large-scale soil moisture reconstructions from a network of tree-ring chronologies have greatly improved our understanding of the spatial and temporal variability in hydroclimate conditions, particularly extremes of both drought and pluvial (wet) events. However, certain regions within these large-scale network reconstructions in the US are modeled by few tree-ring chronologies. Further, many of the chronologies currently publicly available on the International Tree-Ring Data Bank (ITRDB) were collected in the 1980s and 1990s, and thus our understanding of the sensitivity of radial growth to soil moisture in the US is based on a period that experienced multiple extremely severe droughts and neglects the impacts of recent, rapid global change. In this study, we expanded the tree-ring network of the Ohio River valley in the US, a region with sparse coverage. We used a total of 72 chronologies across 15 species to examine how increasing the density of the tree-ring network influences the representation of reconstructing the Palmer Meteorological Drought Index (PMDI). Further, we tested how the sampling date and therefore the calibration period influenced the reconstruction models by creating reconstructions that ended in the year 1980 and compared them to reconstructions ending in 2010 from the same chronologies. We found that increasing the density of the tree-ring network resulted in reconstructed values that better matched the spatial variability of instrumentally recorded droughts and, to a lesser extent, pluvials. By extending the calibration period to 2010 compared to 1980, the sensitivity of tree rings to PMDI decreased in the southern portion of our region where severe drought conditions have been absent over recent decades. We emphasize the need of building a high-density tree-ring network to better represent the spatial variability of past droughts and pluvials. Further, chronologies on the ITRDB need updating regularly to better understand how the sensitivity of tree rings to climate may vary through time.

What Can We Learn from Comparing Glacio-Hydrological Models?

Glacio-hydrological models combine both glacier and catchment hydrology modeling and are used to assess the hydrological response of high-mountain glacierized catchments to climate change. To capture the uncertainties from these model combinations, it is essential to compare the outcomes of several model entities forced with the same climate projections. For the first time, we compare the results of two completely independent glacio-hydrological models: (i) HQsim-GEM and (ii) AMUNDSEN. In contrast to prevailing studies, we use distinct glacier models and glacier initialization times. At first glance, the results achieved for future glacier states and hydrological characteristics in the Rofenache catchment in Ötztal Alps (Austria) appear to be similar and consistent, but a closer look reveals clear differences. What can be learned from this study is that low-complexity models can achieve higher accuracy in the calibration period. This is advantageous especially when data availability is weak, and priority is given to efficient computation time. Furthermore, the time and method of glacier initialization play an important role due to different data requirements. In essence, it is not possible to make conclusions about the model performance outside of the calibration period or more specifically in the future. Hence, similar to climate modeling, we suggest considering different modeling approaches when assessing future catchment discharge or glacier evolution. Especially when transferring the results to stakeholders, it is vital to transparently communicate the bandwidth of future states that come with all model results.

Major Over- and Underestimation of Drought Found in NOAA’s Climate Divisional SPI Dataset

Evaluation of the standardized precipitation index (SPI) dataset published monthly in the National Oceanic and Atmospheric Administration/National Centers for Environmental Information (NOAA/NCEI) climate divisional database revealed that drought frequency is being mischaracterized in climate divisions across the United States. The 3- and 6-month September SPI values were downloaded from the database for all years between 1931 and 2019; the SPI was also calculated for the same time scales and span of years following the SPI method laid out by NOAA/NCEI. Drought frequency is characterized as the total number of years that the SPI fell below −1. SPI values across 1931–90, the calibration period cited by NOAA/NCEI, showed regional patterns in climate divisions that are biased toward or away from drought, according to the average values of the SPI. For both time scales examined, the majority of the climate divisions in the central, Midwest, and northeastern United States showed negative averages, indicating bias toward drought, whereas climate divisions in the western United States, the northern Midwest, and parts of the Southeast and Texas had positive averages, indicating bias away from drought. The standard deviation of the SPI also differed from the expected value of 1. These regional patterns in the NCEI’s SPI values are the result of a different (sliding) calibration period, 1895–2019, instead of the cited standardized period of 1931–90. The authors recommend that the NCEI modify its SPI computational procedure to reflect the best practices identified in the benchmark papers, namely, a fixed baseline period.

Continuous methane concentration measurements at the Greenland ice sheet–atmosphere interface using a low-cost, low-power metal oxide sensor system

In this paper, the performance of a low-cost and low-power methane (CH4) sensing system prototype based on a metal oxide sensor (MOS) sensitive to CH4 is tested in a natural CH4-emitting environment at the Greenland ice sheet (GrIS). We investigate if the MOS could be used as a supplementary measurement technique for monitoring CH4 emissions from the GrIS with the scope of setting up a CH4 monitoring network along the GrIS. The performance of the MOS is evaluated on the basis of simultaneous measurements using a cavity ring-down spectroscopy (CRDS) reference instrument for CH4 over a field calibration period of approximately 100 h. Results from the field calibration period show that CH4 concentrations measured with the MOS are in very good agreement with the reference CRDS. The absolute concentration difference between the MOS and the CRDS reference values within the measured concentration range of approximately 2–100 ppm CH4 was generally lower than 5 ppm CH4, while the relative concentration deviations between the MOS and the CRDS were generally below 10 %. The calculated root-mean-square error (RMSE) for the entire field calibration period was 1.69 ppm (n=37 140). The results confirm that low-cost and low-power MOSs can be effectively used for atmospheric CH4 measurements under stable water vapor conditions. The primary scientific importance of the study is that it provides a clear example of how the application of low-cost technology can enhance our future understanding on the climatic feedbacks from the cryosphere to the atmosphere.

Uncertainty Assessment of Urban Hydrological Modelling from a Multiple Objective Perspective

The uncertainty assessment of urban hydrological models is important for understanding the reliability of the simulated results. To satisfy the demand for urban flood management, we assessed the uncertainty of urban hydrological models from a multiple-objective perspective. A multiple-criteria decision analysis method, namely, the Generalized Likelihood Uncertainty Estimation-Technique for Order Preference by Similarity to Ideal Solution (GLUE-TOPSIS) was proposed, wherein TOPSIS was adopted to measure the likelihood within the GLUE framework. Four criteria describing different urban stormwater characteristics were combined to test the acceptability of the parameter sets. The TOPSIS was used to calculate the aggregate employed in the calculation of the aggregate likelihood value. The proposed method was implemented in the Storm Water Management Model (SWMM), which was applied to the Dahongmen catchment in Beijing, China. The SWMM model was calibrated and validated based on the three and two flood events respectively downstream of the Dahongmen catchment. The results showed that the GLUE-TOPSIS provided a more precise uncertainty boundary compared with the single-objective GLUE method. The band widths were reduced by 7.30 m3/s in the calibration period, and by 7.56 m3/s in the validation period. The coverages increased by 20.3% in the calibration period, and by 3.2% in the validation period. The median estimates improved, with an increase of the Nash–Sutcliffe efficiency coefficients by 1.6% in the calibration period, and by 10.0% in the validation period. We conclude that the proposed GLUE-TOPSIS is a valid approach to assess the uncertainty of urban hydrological model from a multiple objective perspective, thereby improving the reliability of model results in urban catchment.