Modelling of snowmelt runoff across the Himalayan Region

The Snowmelt-Runoff Model (SRM) was used to evaluate the impact of climate change on hydrological aspects of Lidder River Catchment of the Himalayan Region. It was observed that the river has an average discharge of 1082.49 cusecs. The coefficient of determination (R2) was varies in the range 0.90-0.95 during model validation period (2013-2018).The average coefficient of determination 0.926 and average seasonal volume difference (Dv) was obtained (-) 0.83%. The snow melt runoff harvested water can be used to bring 10 per cent more area under irrigation and water use efficiency which can be increased to an extent of 12-15 per cent for sustainable agriculture production in the Himalayan Region.

Assessment of DRAINMOD-NII Model for Prediction of Nitrogen Losses Through Subsurface Drained Sandy Clay Under Cultivation in South West Punjab, India

In the present study, DRAINMOD-NII model was calibrated for the years 2018-2019 and validated for the period 2019-2020 over the two cropping years. The model simulations were statistically evaluated by comparing the measured drain flows and nitrate-nitrogen (NO3-N) with the model simulated drain outflows and nitrate loss. The study results depicted closer agreement between the simulated and observed results for both the calibration and validation periods. The Root Mean Square Error (RMSE) of the drainage rate was 8.88 cm more than observed data,15.41, 0.53 and 0.57 cm were the values recorded for PBIAS, modelling efficiency (NSE) and R2. The similar parameter values for nitrogen load were recorded to be 0.14, 2.76 ,0.84 and 0.88 respectively during the calibration period for rice wheat system. The model was statistically tested during the validation period also, confirming DRAINMOD-NII has the capability to simulate nitrogen losses from the area subjected to subsurface drainage system.

Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model

Cosmic-Ray Neutron Sensing (CRNS) offers a non-invasive method for estimating soil moisture at the field scale, in our case a few tens of hectares. The current study uses the Ensemble Adjustment Kalman Filter (EAKF) to assimilate neutron counts observed at four locations within a 655 km2 pre-alpine river catchment into the Noah-MP land surface model (LSM) to improve soil moisture simulations and to optimize model parameters. The model runs with 100 m spatial resolution and uses the EU-SoilHydroGrids soil map along with the Mualem–van Genuchten soil water retention functions. Using the state estimation (ST) and joint state–parameter estimation (STP) technique, soil moisture states and model parameters controlling infiltration and evaporation rates were optimized, respectively. The added value of assimilation was evaluated for local and regional impacts using independent root zone soil moisture observations. The results show that during the assimilation period both ST and STP significantly improved the simulated soil moisture around the neutron sensors locations with improvements of the root mean square errors between 60 and 62% for ST and 55–66% for STP. STP could further enhance the model performance for the validation period at assimilation locations, mainly by reducing the Bias. Nevertheless, due to a lack of convergence of calculated parameters and a shorter evaluation period, performance during the validation phase degraded at a site further away from the assimilation locations. The comparison of modeled soil moisture with field-scale spatial patterns of a dense network of CRNS observations showed that STP helped to improve the average wetness conditions (reduction of spatial Bias from –0.038 cm3 cm−3 to –0.012 cm3 cm−3) for the validation period. However, the assimilation of neutron counts from only four stations showed limited success in enhancing the field-scale soil moisture patterns.

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.

Land Use/Land Cover Change Impact on Hydrological Process in the Upper Baro Basin, Ethiopia

Understanding the hydrological process associated with Land Use/Land Cover (LU/LC) change is vital for decision-makers in improving human wellbeing. LU/LC change significantly affects the hydrology of the landscape, caused by anthropogenic activities. The scope of this study is to investigate the impact of LU/LC change on the hydrological process of Upper Baro Basin for the years 1987, 2002, and 2017. The Soil Water Assessment Tool (SWAT) model was used for the simulation of the streamflow. The required data for the SWAT model are soils obtained from the Food and Agriculture Organization; Digital Elevation Model (DEM) and LU/LC were obtained from the United States Geological Survey (USGS). The meteorological data such as Rainfall, Temperature, Sunshine, Humidity, and Wind Speeds were obtained from the Ethiopian National Meteorological Agency. Data on discharge were obtained from Ministry of Water, Irrigation and Electricity. Ecosystems are deemed vital. Landsat images were used to classify the LU/LC pattern using ERDAS Imagine 2014 software and the LU/LC were classified using the Maximum Likelihood Algorithm of Supervised Classification. The Sequential Uncertainty Fitting (SUFI-2) global sensitivity method within SWAT Calibration and Uncertainty Procedures (SWAT-CUP) was used to identify the most sensitive streamflow parameters. The calibration was carried out using observed streamflow data from 01 January 1990 to 31 December 2002 and a validation period from 01 January 2003 to 31 December 2009. LU/LC analysis shows that there was a drastic decrease of grassland by 15.64% and shrubland by 9.56% while an increase of agricultural land and settlement by 18.01% and 13.01%, respectively, for 30 years. The evaluation of the SWAT model presented that the annual surface runoff increased by 43.53 mm, groundwater flow declined by 27.58 mm, and lateral flow declined by 5.63 mm. The model results showed that the streamflow characteristics changed due to the LU/LC change during the study periods 1987–2017 such as change of flood frequency, increased peak flows, base flow, soil erosion, and annual mean discharge. Curve number, an available water capacity of the soil layer, and soil evaporation composition factor were the most sensitive parameters identified for the streamflow. Both the calibration and validation results disclosed a good agreement between measured and simulated streamflow. The performance of the model statistical test shows the coefficient of determination (R2) and Nash–Sutcliffe (NS) efficiency values 0.87 and 0.81 for calibration periods of 1990–2002 and 0.84 and 0.76 for the validation period of 2003 to 2009, respectively. Overall, LU/LC significantly affected the hydrological condition of the watershed. Therefore, different conservation strategies to maintain the stability and resilience of the ecosystem are vital.

Assessing model performance by weather regime transitions

<p>Having the ability to stratify a model’s performance by weather type is not only beneficial to a weather forecaster when making decisions, but it is also important for end users, whether they be scientists looking to improve the model, or a customer wishing to know the value of a forecast under a specific set of circumstances.</p><p>At the MET Office, Decider is a tool which assigns a dominant weather type to a set of ensemble members, to predict the probability of a weather type occurring. The weather type is chosen from either a set of 30 or 8 sub-types, where a weather type is pre-determined objectively by clustering a 154 year record of sea level pressure anomaly fields.  </p><p>There is also a record of daily weather type classifications derived from analysis fields and so information of model performance for these weather types could be invaluable in reducing model error if combined with the predictions from Decider.</p><p>Early trials of assessing model performance by weather type revealed that larger errors occur when the weather type persisted for a single day, rather than longer timescales, and so this suggests that it would be beneficial to examine weather type transition periods.</p><p>To examine this, we expand the weather type methodology to include multiple time periods. The current methodology uses 12Z analyses to identify the weather type, and so we first assess model performance as a sensitivity study to the analysis time.</p><p>Transition days are identified when the weather type changes during a pre-defined validation period, which allows separation into either night/day weather type transitions, or a change in weather type over a full 24-hour period.</p><p>We will present early results of this work and demonstrate the impact of model performance when stratifying by regime transitions.</p>

Disaggregation of Daily Rainfall into Hourly Rainfall in an Ungauged Urban Catchment

<p>Nowadays, most of the urban cities and their surrounding ambiances are facing increasing flooding issues. Many times, the cause of urban flooding is improper drainage under increasing rainfall intensity. To properly monitor and manage the drainage system in urban areas, high-resolution rainfall data is required to model the flooding scenarios a priori. However, the high-resolution rainfall data in urban regions to address the urban flooding issues are rarely available, especially in developing countries. To overcome this problem, many studies suggest the use of hourly scale IMERG-FR (Integrated Multi-satellitE Retrievals for GPM-Final Run) data which exhibits good agreement with the ground-truth rainfall measurements. Therefore, this study attempts to utilize area-averaged IMERG-FR hourly data over Bhubaneswar, a data-scarce urban area of eastern India as a benchmark for assessing the performance of six parametric (Bartlett-Lewis Model, BL) and a nonparametric (Method of Fragments, MOF) approaches disaggregating daily scale IMD (India Meteorological Department) rainfall data into hourly scale data. The performance of the considered approaches is evaluated by disaggregating the monsoon months (June-October) rainfall timeseries data for the period 2001-2015 by adopting performance criteria such as root mean square error (RMSE) and percent bias (PBIAS). The rainfall time series data from 2001-2010 and 2011-2015 were used for calibration and validation of the proposed approaches, respectively.</p><p>The obtained RMSE values in the case of the BL approach during calibration and validation period were 2.53 mm and 2.04 mm, respectively. Similarly, RMSE values in the case of the MOF approach during the calibration and validation period were 2.5 mm and 1.87 mm, respectively. This comparison suggests the both of these approaches exhibit nearly the same performance during the calibration period whereas the MOF approach was slightly better than BL during the validation period. The PBIAS estimates for the MOF approach were around -6.6% and 17.3% during the calibration and validation period, respectively, whereas the PBIAS estimates for the BL approach were around 11.25% for calibration and -11.25% for the validation period. From the present evaluation, it could be concluded that though the MOF approach exhibits slightly better performance in terms of RMSE, the BL approach can provide a more balanced performance in terms of PBIAS. As the MOF is a non-parametric approach, it can be applied to a lesser length of daily rainfall time series for disaggregation whereas the BL approach can perform well when its parameters are derived using a good length of rainfall series. Conclusively, this study summarizes the applicability of the BL and MOF approaches for disaggregating course resolution daily scale rainfall to hourly rainfall for the monsoon months in Bhubaneswar using IMERG-FR hourly rainfall data as a benchmark.</p><p><strong>Keywords: </strong>Rainfall; Rainfall disaggregation; Bartlett-Lewis Model (BL); Method of Fragments (MOF); IMERG-FR; IMD.</p>

Assessing a seasonal calibration approach for a small forest catchment in a Mediterranean region North Central Portugal

<p>Hydrological modeling is nowadays a widely used decision making tool to predict watershed behavior in forest areas. A commonly used processed based watershed model is the Soil and Water Assessment Tool (SWAT). SWAT provides comprehensive forest management operations and offers a diversity of adjustable input parameters to simulate complex processes inside a catchment. Nevertheless, one well-known obstacle of SWAT is the poor model performance during dry periods, characterized by low discharge and/or a dried-out catchment, causes by a possible seasonal dependency of input parameter related to climate conditions. Model predictions inherently goes along with uncertainties, linked to a diversity of unknown input parameters and assumptions. Therefore, to minimize model predictions uncertainties the use of an appropriate calibration technique is indispensable. During a conventional calibration process with SWAT model, inputs do not consider seasonal variabilities, by generally using a single parameter set for simulating discharge in a catchment. Although some studies have shown, a significant improvement while using different parameter sets, according to a wet or dry season [1, 2]. However, there is still a knowledge gap in applying such season-based calibration approach, namely under which conditions such approach could improve model predictions. The aim of this study is to determine the parameters which seem to have higher influence under seasonal climate conditions in contrast to season independent parameters, in a semi-managed eucalyptus forest catchment in North Central Portugal. We will use different parameter sets according to a wet and dry period, to improve the discharge simulation and make a model performance more robust. Further to optimize different model scenarios, such as transport processes, that depending on seasonal flow regimes. The climate of the study area is a warm- summer Mediterranean climate dominated by dry, warm and long summers. The hydrological dataset used for the calibration and validation period comprises the hydrological years 2010 to 2016, with a local metrological dataset and discharge measurements from the outlet of the catchment. Global sensitive analysis (GSA) is performed with the Fourier Amplitude Sensitivity Testing (FAST) in SWATplusR [3], for following defined cases, (i) over the complete data period (conventional), (ii) the wet and the (iii) dry season dataset. Whereas for the calibration and the validation period, the dataset is divided by a 4-year calibration and a 2-year validation period. Respectively, a conventional and a season-based calibration is done while using SWATplusR. The GSA results show that the most influencing parameters for the conventional dataset are the curve number (CN2) with a sensitivity of 0.65, followed by the available water capacity of the soil layer (SOL_AWC) with a sensitivity of 0.008. When using the dry season dataset the sensitivity of the CN2 parameter decreases by a factor of 0.45 and SOL_AWC increases by a factor of 5, confirming the hypothesis of an input dependency on seasonal climate conditions.</p><p>[1] Zhang, D. et al., 2015. https://doi.org/10.1016/j.ecolmodel.2015.01.018<br>[2] Muleta, M.K. et al., 2012. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000421<br>[3] Schürz, C., 2019. doi: 10.5281/zenodo.3373859</p>

Stepwise Identification of Influencing Factors and Prediction of Typhoon Precipitation in Anhui Province Based on the Back Propagation Neural Network Model

Typhoon is one of the most frequent meteorological phenomena that covers most of central-eastern China during the summer. Typhoon-induced precipitation is one of the most important water resources, but it often leads to severe flood disasters. Accurate typhoon precipitation prediction is crucial for mitigating typhoon disasters and managing water resources. Anhui Province, located in East China, is a typhoon affected region. Typhoon-related disasters are its major natural disasters. This study aims at developing a new back propagation (BP) neural network model to predict both the typhoon precipitation event and the typhoon precipitation amount. The predictors in the model are identified through correlation analysis of the above two target variables and a large set of candidate variables. We further improve the predictor selection through an iterative approach, which proposes new predictors for the BP model in each iteration by analyzing the differences of candidate predictors between the years with large prediction errors and the normal years. The results show that the accuracy of the BP-based summer typhoon event prediction model in the simulation period from 1957 to 2006 is 100%, and its accuracy in the validation period from 2007 to 2016 is 90%. In addition, the absolute value of the mean relative error predicted by the typhoon precipitation amount model for the simulation period is 20.9%. A significant error can be found in 2000 as the mechanism of typhoon precipitation in this year is different from that of other normal years. The error in 2000 is probably caused by the impact of vertical shear anomalies over the western Pacific which hinders the development of typhoon embryos. Additionally, the absolute value of the mean relative error predicted by the typhoon precipitation amount model in the validation period is 14.2%. A significant error also can be found in 2009, probably due to the influence of the asymmetry in the typhoon cloud system.

Generalised Linear Models for Prediction of Dissolved Oxygen in a Waste Stabilisation Pond

Due to simplicity and low costs, waste stabilisation ponds (WSPs) have become one of the most popular biological wastewater treatment systems that are applied in many places around the globe. Increasingly, pond modelling has become an interesting tool to improve and optimise their performance. Unlike process-driven models, generalised linear models (GLMs) can deliver considerable practical values in specific case studies with limited resources of time, data and mechanistic understanding, especially in the case of pond systems containing vast complexity of many unknown processes. This study aimed to investigate the key driving factors of dissolved oxygen variability in Ucubamba WSP (Ecuador), by applying and comparing numerous GLMs. Particularly, using different data partitioning and cross-validation strategies, we compared the predictive accuracy of 83 GLMs. The obtained results showed that chlorophyll a had a strong impact on the dissolved oxygen (DO) level near the water surface, while organic matter could be the most influential factor on the DO variability at the bottom of the pond. Among the 83 models, the optimal models were pond- and depth-specific. Specifically, among the ponds, the models of MPs predicted DO more precisely than those of facultative ponds; while within a pond, the models of the surface performed better than those of the bottom. Using mean absolute error (MAE) and symmetric mean absolute percentage error (SMAPE) to represent model predictive performance, it was found that MAEs varied in the range of 0.22–2.75 mg L−1 in the training period and 0.74–3.54 mg L−1 in the validation period; while SMAPEs were in the range of 2.35–38.70% in the training period and 10.88–71.62% in the validation period. By providing insights into the oxygen-related processes, the findings could be valuable for future pond operation and monitoring.