Estimation of River Discharge from Satellite Observations

The role of streamflow is very important in any type of hydrologic. For very effective flood routing and hydraulic structure design, it is important to have a large dataset of past years. We now have a conceptual rainfall-runoff model that can predict streamflow based on pre-existing datasets. Because there is no or very little observed data in un-gauged basins, calibrating these models to predict daily streamflow becomes difficult. Nowadays, parameters for example river width can be observed using satellite images, and some studies show a promising associated relation between discharge and river width. The suggested study demonstrates a method for calculating streamflow from river width extracted with the help of satellite imagery. To predict streamflow, hydrological models are calibrated using river width instead of in site observed streamflow, and for estimating uncertainty Generalized Likelihood Uncertainty Estimation (GLUE) is used. For validation, the suggested method is implemented in the Kharun river basin situated in the Chhattisgarh state of India. The obtained Nash-Sutcliffe efficiency is 92.6 % for simulated river discharge in 2019-2020 at the 50% quantile, which is promising.

New functional topographic, lithology and climatic indices to define shallow groundwater systems in (un)gauged basins

<p>The dynamics of the rainfall-runoff processes are complex and variable both spatially and temporally. There is a rich literature on physical representation of streamflow generation processes, such as saturation excess overland flow, often at small scales. Yet, continental-scale estimations of the streamflow generation processes in zones with shallow groundwater systems are still poor. This has led to inability of earth system models or large-scale hydrologic models to correctly simulate stream flows at (un)gauged basins with high potential for the presence of saturation excess overland flow. Zones with shallow groundwater have a direct impact on the hydrologic response of rainfall events. Depending on the subsurface storage, climate signals and topography, they can enhance the overland flow, or act as a buffer zone to flatten the flood hydrographs. <br>We have introduced new indices, inspired by the concept of hydrologic function, that include the interactions amongst climatic and geophysical characteristics (soil parameters, topography and lithology) to delineate zones of shallow groundwater over the United States and Canada. We have evaluated and tested the ability of these indices in locating high-resolution zones of shallow groundwater against in-situ observations of water table depth. The knowledge of the spatial pattern of shallow groundwater zones at (un)gauged basins allows an accurate inclusion of hydrologic connectivity in earth system models or large-scale hydrologic models, improving their prediction of stream peak flow. Furthermore, as a significant part of incoming precipitation is transformed to overland flow due to oversaturation, these datasets could be introduced as a useful indicator of areas with flood and erosion susceptibility.</p>

Comparison of NCEP-CFSR and CMADS for Hydrological Modelling Using SWAT in the Muda River Basin, Malaysia

Identification of reliable alternative climate input data for hydrological modelling is important to manage water resources and reduce water-related hazards in ungauged or poorly gauged basins. This study aims to evaluate the capability of the National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) and China Meteorological Assimilation Driving Dataset for the Soil and Water Assessment Tool (SWAT) model (CMADS) for simulating streamflow in the Muda River Basin (MRB), Malaysia. The capability was evaluated in two perspectives: (1) the climate aspect—validation of precipitation, maximum and minimum temperatures from 2008 to 2014; and (2) the hydrology aspect—comparison of the accuracy of SWAT modelling by the gauge station, NCEP-CFSR and CMADS products. The results show that CMADS had a better performance than NCEP-CFSR in the climate aspect, especially for the temperature data and daily precipitation detection capability. For the hydrological aspect, the gauge station had a “very good” performance in a monthly streamflow simulation, followed by CMADS and NCEP-CFSR. In detail, CMADS showed an acceptable performance in SWAT modelling, but some improvements such as bias correction and further SWAT calibration are needed. In contrast, NCEP-CFRS had an unacceptable performance in validation as it dramatically overestimated the low flows of MRB and contains time lag in peak flows estimation.

A methodology to estimate flow duration curves at partially ungauged basins

The flow duration curve (FDC) of streamflow at a specific site has a key role in the knowledge on the distribution and characteristics of streamflow at that site. The FDC gives information on the water regime, providing information to optimally manage the water resources of the river. In spite of its importance, because of the lack of streamflow gauging stations, the FDC construction can be a not straightforward task. In partially gauged basins, FDCs are usually built using regionalization among the other methods. In this paper we show that the FDC is not a characteristic of the basin only, but of both the basin and the weather. Different weather conditions lead to different FDCs for the same catchment. The differences can often be significant. Similarly, the FDC built at a site for a specific period cannot be used to retrieve the FDC at a different site for the same time window. In this paper, we propose a new methodology to estimate FDCs at partially gauged basins (i.e., target sites) using precipitation data gauged at another basin (i.e., donor site). The main idea is that it is possible to retrieve the FDC of a target period of time using the data gauged during a given donor time period for which data are available at both target and donor sites. To test the methodology, several donor and target time periods are analyzed and results are shown for different sites in the USA. The comparison between estimated and actually observed FDCs shows the reasonability of the approach, especially for intermediate percentiles.

Can assimilating snow monitoring information offset the adverse effects of precipitation data scarcity in hydrological modelling applications?

<p>The applicability of the Canadian Precipitation Analysis products known as the Regional Deterministic Precipitation Analysis (CaPA-RDPA) for hydrological modelling in boreal watersheds in Canada, which are constrained with shortage of precipitation information, has been the subject of a number of recent studies. The northern and mid-cordilleran alpine, sub-alpine, and boreal watersheds in Yukon, Canada, are prime examples of such Nordic regions where any hydrological modelling application is greatly scrambled due to lack of accurate precipitation information. In the course of the past few years, proper advancements were tailored to resolve these challenges and a forecasting system was designed at the operational level for short- to medium-range flow and inflow forecasting in major watersheds of interest to Yukon Energy. This forecasting system merges the precipitation products from the North American Ensemble forecasting System (NAEFS) and recorded flows or reconstructed reservoir inflows into the HYDROTEL distributed hydrological model, using the Ensemble Kalman Filtering (EnKF) data assimilation technique. In order to alleviate the adverse effects of scarce precipitation information, the forecasting system also enjoys a snow data assimilation routine in which simulated snowpack water content is updated through a distributed snow correction scheme. Together, both data assimilation schemes offer the system with a framework to accurately estimate flow magnitudes. This robust system not only mitigates the adverse effects of meteorological data constrains in Yukon, but also offers an opportunity to investigate the hydrological footprint of CaPA-RDPA products in Yukon, which is exactly the motivation behind this presentation. However, our overall goal is much more comprehensive as we are trying to elucidate whether assimilating snow monitoring information in a distributed hydrological model could meet the flow estimation accuracy in sparsely gauged basins to the same extent that would be achieved through either (i) the application of precipitation analysis products, or (ii) expanding the meteorological network. A proper answer to this question would provide us with valuable information with respect to the robustness of the snow data assimilation routine in HYDROTEL and the intrinsic added-value of using CaPA-RDPA products in sparsely gauged basins of Yukon.</p>

Merging of satellite rainfall estimates from diverse sources with K nearest neighbour in sparsely gauged basins

<p>Satellite based rainfall estimates (SBRE) are used as an alternative to gauge rainfall in hydrological studies particularly for basins with data issues. However, these data products exhibit errors which cannot be always corrected by bias correction methods such as Ratio Bias Correction (RBC). Data fusion or data merging can be a potentially good approach in merging various SBREs to obtain a fused dataset, which can benefit from all the data sources and may minimise the error in rainfall estimates. Data merging methods which are commonly applied in meteorology and hydrology are: Arithmetic merging method (AMM), Inverse error squared weighting (IESW) and Error variance (EV). Among these methods EV is popular, which merges bias corrected SBREs using the minimisation of variance principle.</p><p>In this research we propose using K Nearest Neighbour (KNN) as a data merging method. KNN has a particular advantage as it does not depend upon any specific statistical model to merge data and presents a great flexibility as the value of K (the number of neighbours to be chosen) can be varied to suit the purpose (for example, choosing different K values for different seasons). In this research it is proposed to compute the distances of bias corrected SBREs of the training data from the gauge data and to assign the SBRE with the minimum distance as the class C where C = 1, 2, 3,…, number of SBREs. In validation each data point consisting of a value of each SBRE may be compared with the data points from the training set and the class of the data point(s) closest to this data point is assigned as the class of the validation data point.</p><p>The KNN approach as a data merging method was applied to the Indus basin in Pakistan. Three satellite rainfall products CMORPH, PERSIANN CDR and TRMM 3B42 with 0.25° x 0.25° spatial and daily temporal resolution were used. Based on the climatic and physiographic features the Indus basin was divided into four zones. Rainfall products were compared at daily, weekly, fortnightly, monthly and seasonally whereas spatial scales were gauge location, zonal scales and basin scale. The RBC method was used to correct the bias. The KNN method with K=1, 3 and 5 was used and compared with other merging methods namely AMM, IESW and EV. The results were compared in two seasons i.e. non-wet and wet season. AMM and EV methods performed similarly whereas IESW performed poorly at zonal scales. KNN merging method outperformed all other merging methods and gave lowest error across the basin. The daily normalised root mean square error at the Indus basin scale was reduced to 0.3, 0.45 and 0.45 respectively with KNN, AMM and EV whereas this error was 0.8, 0.65 and 0.53 respectively in CMORPH, PERSIANN CDR and TRMM datasets. The KNN merged product gave lowest error at daily scale in calibration and validation period which justifies that merging with KNN improves rainfall estimates in sparsely gauged basins.</p><p> </p><p><strong>Key words:</strong> Merging, data fusion, K nearest neighbour, KNN, error variance, Indus.</p>

Flood Inundation Mapping of the Sparsely Gauged Large-Scale Brahmaputra Basin Using Remote Sensing Products

Sustainable water management is one of the important priorities set out in the Sustainable Development Goals (SDGs) of the United Nations, which calls for efficient use of natural resources. Efficient water management nowadays depends a lot upon simulation models. However, the availability of limited hydro-meteorological data together with limited data sharing practices prohibits simulation modelling and consequently efficient flood risk management of sparsely gauged basins. Advances in remote sensing has significantly contributed to carrying out hydrological studies in ungauged or sparsely gauged basins. In particular, the global datasets of remote sensing observations (e.g., rainfall, evaporation, temperature, land use, terrain, etc.) allow to develop hydrological and hydraulic models of sparsely gauged catchments. In this research, we have considered large scale hydrological and hydraulic modelling, using freely available global datasets, of the sparsely gauged trans-boundary Brahmaputra basin, which has an enormous potential in terms of agriculture, hydropower, water supplies and other utilities. A semi-distributed conceptual hydrological model was developed using HEC-HMS (Hydrologic Modelling System from Hydrologic Engineering Centre). Rainfall estimates from Tropical Rainfall Measuring Mission (TRMM) was compared with limited gauge data and used in the simulation. The Nash Sutcliffe coefficient of the model with the uncorrected rainfall data in calibration and validation were 0.75 and 0.61 respectively whereas the similar values with the corrected rainfall data were 0.81 and 0.74. The output of the hydrological model was used as a boundary condition and lateral inflow to the hydraulic model. Modelling results obtained using uncorrected and corrected remotely sensed products of rainfall were compared with the discharge values at the basin outlet (Bahadurabad) and with altimetry data from Jason-2 satellite. The simulated flood inundation maps of the lower part of the Brahmaputra basin showed reasonably good match in terms of the probability of detection, success ratio and critical success index. Overall, this study demonstrated that reliable and robust results can be obtained in both hydrological and hydraulic modelling using remote sensing data as the only input to large scale and sparsely gauged basins.

Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product

No synthesized global gridded runoff product, derived from multiple sources, is available, despite such a product being useful for meeting the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-sample tests to examine the success of the dissimilarity approach, and we confirm that the weighted product performs better than its 11 constituent products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly timescales, and includes time-variant uncertainty, for the period 1980–2012 on a 0.5∘ grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents the seasonal runoff cycle for most of the globe well. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and will be freely available for download on https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f9617_9854_8096_5291 (last access: 31 January 2019).

Modelling Snowmelt in Ungauged Catchments

Temperature-based snowmelt models are simple to implement and tend to give goodresults in gauged basins. The situation is, however, different in ungauged basins, as the lack ofdischarge data precludes the calibration of the snowmelt parameters. The main objective of thisstudy was therefore to assess alternative approaches. This study compares the performance oftwo temperature-based snowmelt models (with and without an additional radiation term) and twoenergy-balance models with different data requirements in 312 catchments in the US. It considersthe impact of: (i) the meteorological forcing, by using two gridded datasets (Livneh and MERRA-2),(ii) different approaches for calibrating the snowmelt parameters (an a priori approach and onebased on Snow Data Assimilation System (SNODAS), a remote sensing-based product) and (iii) theparameterization and structure of the hydrological model used for transforming the snowmelt signalinto streamflow at the basin outlet. The results show that energy-balance-based approaches achievethe best results, closely followed by the temperature-based model including a radiation term andcalibrated with SNODAS data. It is also seen that data availability and quality influence the rankingof the snowmelt models.