Journal of Hydrologic Engineering

Probable maximum precipitation (PMP) is the design criterion for many hydraulic and hydrologic structures. Despite its significance in water resource engineering, estimation of PMP values carries uncertainties. On the other hand, recent research suggests that PMP values are subject to change as a consequence of climate change. To study these issues, PMP estimates from three precipitable water calculation methods were first compared to evaluate sensitivity of PMP values to the selected estimation methods. All required data for PMP estimates were obtained from climate models’ outputs to ensure consistency in comparing results. Seven climate simulations of the 1961–1990 horizon were used to produce PMP values for three watersheds in the south, center, and north of the Province of Québec (Canada). To analyze the future climate influence on PMP, climate projections of the 2041–2070 horizon were used. Results show that a general increase in PMP in a future climate for the three watersheds studied is ubiquitous in all methods employed, reinforcing the conclusion that PMP will be rising in the future in these regions.

The Soil Conservation Service Curve Number (SCS-CN) method is widely used in conceptual rainfall-runoff models for describing the runoff response with a curve, which is a function of the cumulative storm rainfall and antecedent wetness conditions. To improve the SCS-CN method, a new distribution function was recently proposed to unify the surface runoff modeling of the SCS-CN method and probability-distributed functions in the variable infiltration capacity (VIC) and Xin’anjiang models. This study aims to verify the new distribution function in a conceptual rainfall-runoff model and in the Soil and Water Assessment Tool (SWAT) by using real catchments. The Xunhe River basin in China and other basins in the United States were used as case studies. Results show that more observed variability in streamflow is captured when using the new spatial distribution function of soil water storage capacity in the conceptual runoff model. Specifically, there is a 9.8% average increase in the Nash-Sutcliffe efficiency (NSE), while simultaneously reducing the bias and mean relative absolute error (MRAE). When using the new distribution in SWAT, the model is able to better estimate the observed streamflow as indicated by higher NSE values for most of the basins. Akaike information criterion (AIC) is used for validating the goodness-of-fit when the number of parameters and model structure change. Further findings suggest that the estimated variance is more sensitive to the value of the new shape parameter $a$ when soil water content is low in the early stage of rainfall. Therefore, the proposed new distribution function is shown to be effective in improving the accuracy of simulating streamflow for both conceptual and SWAT models.

Poyang Lake is the largest freshwater lake in China and is internationally recognized as an important lacustrine and wetland ecosystem with abundant biodiversity. Since 2003, the average lake water level has significantly declined by 1.35 m during the dry season. Quantifying the contribution of different factors to water level decline is necessary for lake management. The impact factors mainly include hydrological and topographic changes in terms of the decreased inflow from the five tributaries, lower water level in the Yangtze River, the decline of the lakebed, and the increased discharge ability of the lake. Back-propagation neural networks have been constructed to simulate the lake water level, considering preimpact and postimpact streamflow and topographic scenarios. The simulations verified the robustness and applicability of the networks and indicated that the streamflow and topographic changes contributed approximately 34% and 66% to the decline in the water level, respectively. The Three Gorges Reservoir exerts various impacts on the water level of Poyang Lake. The reservoir sedimentation causes riverbed erosion and lowers the downstream water level. The water release increases the downstream flow and is conducive to elevating the lake water level. But the impact is marginal because it is diluted by the topographic changes and the decreased runoff of the subbasins along the middle and lower Yangtze River. These results provide useful information to guide strategies for wetland and lacustrine ecological protection associated with water level fluctuations.

The Qinghai-Tibet Plateau (QTP) glaciers have been consistently retreating for decades and accelerating their retreat in recent years. It would impact the river water resources that originate from the QTP, such as the Yangtze River Basin (YRB). Therefore, there is an urgent need to study the relationship between QTP glacier melt and land water storage changes (LWSC) across the YRB. Here, the investigation will be inferred by combining data from the Gravity Recovery and Climate Experiment (GRACE); the Ice, Cloud, and land Elevation Satellite (ICESat); a 3-arc-second digital elevation model of the Shuttle Radar Terrestrial Mission (SRTM); a hydrological model; and in situ hydrological and weather stations. The results indicate that (1) the glacier melt in the Eastern Tibetan Plateau (ETP) is a result of the increasing temperature and the LWSC in the YRB are increasing, (2) the contribution of ETP glacier melt to the LWSC in the YRB was close to 20% during the period 2003–2009, (3) the maximum correlation between them is 0.45 with a lag of approximately 9 months, and (4) the LWSC in the YRB are primarily from precipitation and secondarily from the ETP glacier melt. Our research provides important information for applications within China’s South-to-North Water Transfer Project and water resource management.