Hydrological Impacts Of Climate Change – Challenges, Uncertainty And Limitations

How climate change impacts water resources in the future is an important question that all hydrologists want to have an answer. Climate prediction scenarios are available from many Global Circulation Models for the 21st century. These prediction datasets are typically used as input to a hydrological model for simulating impacts on hydrology, particularly river runoff, evaporation, and storage changes. Because hydrological models are usually run on a much smaller resolutions than climate models, the climate prediction datasets are usually downscaled to represent local climate for using in a hydrological model. The uncertainty in the GCMs, downscaling and hydrological models makes the process complicated and heavily restricts our ability to make predictions of hydrological impacts. This becomes more challenging in a mountainous catchment where the availability of hydro-climatic data are limited. We illustrated some of these issues and their impacts on hydrological simulations using two catchments from the Himalayan region: the Koshi River (~58,000 km2), Nepal, and the source region of the Yellow River (~120,000 km2), China. Climate predictions used are from a number of GCMs participated in the Coupled Model Intercomparison Project (CMIP3). In both examples we used process-based distributed hydrological models: the Soil and Water Assessement Tool (SWAT) for the Koshi and WaSiM for the Yellow River

Twenty-three unsolved problems in hydrology (UPH) – a community perspective

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focussed on process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come

Improved first-order second moment method for uncertainty estimation in flood forecasting

The first-order second moment (FOSM) method is widely used inuncertainty analysis. This method uses a linearization of the function that relates theinput variables and parameters to the output variables. This simplification occasionallyleads to problems when the mean value of the input variable is close to a local orglobal maximum or minimum value of the function. In this case, the FOSM computesartificially a zero uncertainty because the first derivative of the function is equal tozero. An improvement to the FOSM is proposed, whereby a parabolic reconstructionis used instead of a linear one. The improved FOSM method is applied to a floodforecasting model on the Loire River (France). Verification of the method using theMonte Carlo technique shows that the improved FOSM allows the accuracy of theuncertainty assessment to be increased substantially, without adding a significantburden in computation. The sensitivity of the results to the size of the perturbation isalso analysed

Hydrological model calibration with streamflow and remote sensing based evapotranspiration data in a data poor basin

Conventional calibration methods adopted in hydrological modelling are based on streamflow data measured at certain river sections. However, streamflow measurements are usually sparse and, in such instances, remote-sensing-based products may be used as an additional dataset(s) in hydrological model calibration. This study compares two main calibration approaches: (a) single variable calibration with streamflow and evapotranspiration separately, and (b) multi-variable calibration with both variables together. Here, we used remote sensing-based evapotranspiration data from Global Land Evaporation: the Amsterdam Model (GLEAM ET), and measured streamflow at four stations to calibrate a Soil and Water Assessment Tool (SWAT) and evaluate the performances for Chindwin Basin, Myanmar. Our results showed that when one variable (either streamflow or evapotranspiration) is used for calibration, it led to good performance with respect to the calibration variable but resulted in reduced performance in the other variable. In the multi-variable calibration using both streamflow and evapotranspiration, reasonable results were obtained for both variables. For example, at the basin outlet, the best NSEs (Nash-Sutcliffe Efficiencies) of streamflow and evapotranspiration on monthly time series are, respectively, 0.98 and 0.59 in the calibration with streamflow alone, and 0.69 and 0.73 in the calibration with evapotranspiration alone. Whereas, in the multi-variable calibration, the NSEs at the basin outlet are 0.97 and 0.64 for streamflow and evapotranspiration, respectively. The results suggest that the GLEAM ET data, together with streamflow data, can be used for model calibration in the study region as the simulation results show reasonable performance for streamflow with an NSE > 0.85. Results also show that many different sets of parameter values (‘good parameter sets’) can produce results comparable to the best parameter set

Effects of different precipitation inputs on streamflow simulation in the Irrawaddy River Basin, Myanmar

Study region: The Irrawaddy River Basin, Myanmar. Study focus: Precipitation is the most important input variable to numerically simulate the hydrological responses of a river basin. Nowadays, a number of precipitation data products with different spatial and temporal resolutions are available. However, the accuracy of these products may vary greatly and the variations may themselves differ in different river basins. Such differences have direct implications on the use of these datasets in hydrological modelling. Here, using a hydrological model, we investigated the effects of four precipitation datasets (in-situ gauge precipitation with and without interpolation, PERSIANN-CDR, and CHIRPS) on streamflow simulations in the Irrawaddy Basin in Myanmar. New hydrological insights for the study region: We identified considerable differences in streamflow simulation with the use of different precipitation inputs. The four datasets showed varied annual and seasonal precipitation values over the basin. Although the gauge density within the study area is very low, streamflow simulations forced with interpolated gauge data outperformed the models forced with other datasets. However, simulations forced with CHIRPS and PERSIANN-CDR also showed good results in most cases in terms of Nash Efficiency and R2, but mostly with high biases. In calibration, the four precipitation inputs resulted in varied best-fitted parameter values and ranges. All the above observations indicate that the selection of suitable precipitation input(s) is necessary for an accurate investigation of the hydrological responses of any given basin. Keywords: Irrawaddy River Basin, Spatial and temporal variabilities of precipitation, Streamflow simulatio

Model-Based Assessment of Preventive Drought Management Measures’ Effect on Droughts Severity

Preventive Drought Management Measures (PDMMs) aim to reduce the chance of droughts and minimize drought-associated damages. Selecting PDMMs is not a trivial task, and it can be asserted that actual contributions to drought alleviation still need to be adequately researched. This study evaluates the effects of three potential PDMMs, namely, rainwater harvesting ponds, forest conservation, and check dams, on agricultural and hydrological drought severity. The Soil Water Assessment Tool is used for hydrological modeling and representing PDMMs. The threshold level method is applied to analyze droughts and evaluate the impact of PDMMs on drought severity. Findings show that rainwater harvesting ponds applied on agricultural land reduce the severity of agricultural droughts and hydrological droughts, particularly during the first months of the drought events observed in the rainy season. Results also reveal that forest conservation contributes to reducing the severity of hydrological droughts by up to 90%. Finally, check dams and ponds in upstream subbasins considerably reduce agricultural and hydrological drought severity in the areas where the structures are applied; however, they exacerbate drought severity downstream. The analysis was developed in the Torola River Basin (El Salvador) for the period spanning 2004 to 2018