The Low Flow Assessment of Padma River in Bangladesh

Low flow or Environmental Flow (EF) assessment is vital to ensure the river and ecosystem remain healthy. Both natural and human interventions might alter a river. Therefore, this study presents EF requirements of the famous Hilsa breeding center in the Padma River, Bangladesh, by applying the Hydrologic Engineering Centers River Analysis System (HEC-RAS) for discharge and water surface levels simulations at different stations. The frequency analysis of 20 years of historical data, spanning 2000-2019, used the Log-Pearson Type III (LP-III) distribution method, while the one-dimensional unsteady flow simulation was performed for the last 10 years (i.e., 2012-2019). Subsequently, the HEC-RAS simulated water level values reasonably correlated with the field observations at four stations, namely Baruria Transit, Mawa, Tarpasha, Sureswar, with Coefficient of determination R2=0.86, 0.83, 0.92, and 0.74, alongside simulated minimum water surface levels of 1.57 m, 0.37 m, 0.30 m, and 0.27 m, respectively. Also, the Baruria Transit and Mawa had simulated flows that reasonably correlated with the field observations at R2=0.70 and 0.61, with a simulated minimum flow of 3849.51 m3/s and 3789.14 m3/s, respectively. The minimum flow according to the frequency analysis was 4017 m3/s, 3685 m3/s, 3449 m3/s, 3229 m3/s, and 3113 m3/s at Baruria Transit and 3304 m3/s, 2781 m3/s, 2438 m3/s, 2141 m3/s, 1992 m3/s at Mawa station in 5, 10, 20, 50 and 100 years return periods, respectively. This study overlooked to report the ongoing investigations into the water quality issues. Thus, this study is expected to guide the required EF quantity towards a healthy Hilsha fish habitat and surface water source for drinking purposes in this studied river. The stated method is also applicable to other similar rivers around the world.

Clustered, Stacked and Imbricated Large Coastal Rock Clasts on Ludao Island, Southeast Taiwan, and Their Application to Palaeotyphoon Intensity Assessment

This work investigated the characteristics of a boulder field on the exposed south east coast of Ludao Island (Green Island) in southern Taiwan. Although the region regularly experiences seasonal Pacific typhoons, fieldwork on Ludao was prompted following the double-strike of Typhoon Tembin in August 2012, which followed an unusual looping track and was one of the strongest storms to affect the island in recent decades. In Wen Cuen Bay, large limestone and volcanic clasts (103–105 kg) occur both as isolated individuals and also grouped into distinct clusters across the gently-sloping emerged reef platform of Holocene age. Some individuals reach megaclast proportions. Observations revealed limited evidence for the production of new coastal boulders by Typhoon Tembin. However, clustering, stacking and notable imbrication of old large clasts provide evidence for multiple high-energy palaeoevents. Stacking and imbrication are significant depositional features, implying that (partial) lifting by wave transport was responsible. Boulders deposited by Typhoon Tembin suggest that storm produced minimum flow velocities of 3.2–5.1 m/s. This range of minimum flow velocity (MFV) values is lower than the 4.3–13.8 m/s range inferred from the pre-Tembin boulders, which indicates that older storm washovers must have been stronger, judging from their ability to stack and imbricate large clasts. One explanation for high upper values of palaeoevent MFVs is that localized funnelling of water flow through narrow relict channels (inherited spur-and-groove morphology, oriented perpendicular to the modern reef edge) concentrates onshore flow energy into powerful confined jets. Support for this hypothesis is the positioning and train-of-direction of the main imbricated boulder cluster at the landward head of one such feature. Geomorphic controls amplifying wave-driven flow velocities across the emerged Holocene reef mean that a palaeotyphoon origin is sufficient for explaining large clast stacking and imbrication, without the need to invoke a tsunami hypothesis.

Influence of Abandonment Pressure on Recoverable Reserves, Special Application to the Depleted Dnipro-Donetsk Basin Reservoirs

Volumetric gas reservoirs are driven by the compressibility of gas and a formation rock, and the ultimate recovery factor is independent of the production rate but depends on the reservoir pressure. The gas saturation in the volumetric reservoir is constant, and the gas volume is reduced causing pressure drop in the reservoir. Due to this reason, it is crucial to minimize the abandonment pressure to the lowest possible level. Concerning Dnipro-Donetsk Basin (DDB) gas reservoirs, it is widespread to recover sometimes more than 90% of the OGIP. Often, OGIP was estimated not considering lower permeability gas layers due to inaccurate logging equipment used in the past, causing that such layers were not included in the total netpay. This is one of the reasons for OGIP overestimation and higher recovery factors. On many P/Z graphs, we observe that at certain drawdown, lower permeability reservoirs kick in lifting up P/Z plot curve. Abandonment pressure is a major factor in determining recovery efficiency. Permeability and skin are usually the most critical factors in determining the magnitude of the abandonment pressure. Reservoirs with low permeability will have higher abandonment pressures than reservoirs with high permeability. A specific minimum flow rate must be sustained to keep the well unloading process, and a higher permeability will permit this minimum flow rate at lower reservoir pressure. Abandonment pressure will depend on wellhead pressure, friction and hydrostatic pressures in the system, pressure drop in reservoir, and pressure drop due to skin. This last factor is often neglected, which sometimes leads to a significant reduction of the recovery factor. It is common practice that skin factor and pressure drop due to the skin are solved with well stimulation. Also, well stimulation has its limits concerning the level of reservoir pressure. It is very common that the stimulation effect of low reservoir pressure well is negligible or even negative. This is caused by the minimum required drawdown to flow back a stimulating aqueous fluid out of the reservoir. The required minimum drawdown is caused by the Phase Trapping Coefficient (PTC), which drives reservoir stimulation fluid cleaning behavior. For water drive gas reservoirs, Cole (1969) suggests that the recovery is substantially less than recovery from bounded gas reservoirs. As a rule of thumb, recovery from a water-drive reservoir will be approximately 50 to 75% of the initial gas in place. The structural location of producing wells and the degree of water coning are essential considerations in determining ultimate recovery. In the cases studied in this paper, we consider gas and rock expansion reservoir energy, if abandonment pressure needs to be coupled with a water drive, then it is recommended to use a numerical, not analytical approach.

Incremental Minimum Flow Algorithms

There are various situations in which real-world problems can be modeled and solved as minimum flow problems. Sometimes, in these situations, minor data changes may occur, leading to corresponding changes of the networks in which the practical problems are modeled as flow problems, such as slight variations in capacity or lower bound. For instance, the capacity or the lower bound of an arc may increase or decrease in time, leaving one with no other choice than finding the new minimum network flow. Given both the various ways in which the networks can be changed and the high frequency of these changes, it is desirable to find as fast a computation method for minimum flow as possible. This paper is focused on the cases that concern increasing and decreasing the capacity or the lower bound of an arc. For these cases, both the minimum flow algorithms and the dynamic minimum flow algorithms that are already known are inefficient. Our incremental algorithms for determining minimum flow in the modified network are more efficient than both the above-mentioned types of algorithms. The proposed method starts from the initial network minimum flow and solves the minimum flow problem in a significantly faster way than recalculating the new network minimum flow starting from scratch.

Regulation of Fresh Water Uses

Chapter 2 traces the evolution of fresh water regulation. It identifies the various uses of fresh water that have been subject to legal rules, including boundary delimitation along international watercourses, navigation, fishing, irrigation, energy production, other industrial uses, and recreational purposes. Areas where conflicts of uses arise are also highlighted, and the way in which these are sometimes resolved by the law is explained, with an emphasis on the importance of human needs and the notion of minimum flow. The major treaties that purport to govern international watercourses, such as the UN Watercourses Convention of 1997, which entered into force in 2014, as well as other sources of fresh water and their accompanying legal regimes, are similarly presented.

Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

This study aims to detect non-stationarity of the maximum and minimum streamflow regime in Tamsui River basin, northern Taiwan. Seven streamflow gauge stations, with at least 27-year daily records, are used to characterize annual maximum 1- and 2-day flows and annual minimum 1-, 7-, and 30-day flows. The generalized additive models for location, scale, and shape (GAMLSS) are used to dynamically detect evolution of probability distributions of the maximum and minimum flow indices with time. Results of time-covariate models indicate that stationarity is only noted in the 4 maximum flow indices out of 35 indices. This phenomenon indicates that the minimum flow indices are vulnerable to changing environments. A 16-category distributional-change scheme is employed to classify distributional changes of flow indices. A probabilistic distribution with complex variations of mean and variance is prevalent in the Tamsui River basin since approximate one third of flow indices (34.3%) belong to this category. To evaluate impacts of dams on streamflow regime, a dimensionless index called the reservoir index (RI) serves as an alternative covariate to model nonstationary probability distribution. Results of RI-covariate models indicate that 7 out of 15 flow indices are independent of RI and 80% of the best-fitted RI-covariate models are generally worse than the time-covariate models. This fact reveals that the dam is not the only factor in altering the streamflow regime in the Tamsui River, which is a significant alteration, especially the minimum flow indices. The obtained distributional changes of flow indices clearly indicate changes in probability distributions with time. Non-stationarity in the Tamsui River is induced by climate change and complex anthropogenic interferences.