Water resources management, in particular flood control, in the Lancang‐Mekong River Basin (LMRB) faces two key challenges in the 21st century: climate change and dam construction. A large‐scale distributed Geomorphology‐Based Hydrological Model coupled with a simple reservoir regulation model (GBHM‐LMK‐SOP) is used to investigate the relative effects of climate change and dam construction on the flood characteristics in the LMRB. Results suggest an increase in both flood magnitude and frequency under climate change, which is more severe in the upstream basin and increases over time. However, stream regulation by dam reduces flood risk consistently throughout this century, with more obvious effects in the upstream basin where larger reservoirs will be located. The flood mitigation effect of dam regulation dominates over the flood intensification effect of climate change before 2060, but the latter emerges more prominently after 2060 and dominates the flood risk especially in the lower basin.

The Mekong River is the most important river in Southeast Asia. It has increasingly suffered from water-related problems due to economic development, population growth and climate change in the surrounding areas. In this study, we built a distributed Geomorphology-Based Hydrological Model (GBHM) of the Mekong River using remote sensing data and other publicly available data. Two numerical experiments were conducted using different rainfall data sets as model inputs. The data sets included rain gauge data from the Mekong River Commission (MRC) and remote sensing rainfall data from the Tropic Rainfall Measurement Mission (TRMM 3B42V7). Model calibration and validation were conducted for the two rainfall data sets. Compared to the observed discharge, both the gauge simulation and TRMM simulation performed well during the calibration period (1998–2001). However, the performance of the gauge simulation was worse than that of the TRMM simulation during the validation period (2002–2012). The TRMM simulation is more stable and reliable at different scales. Moreover, the calibration period was changed to 2, 4, and 8 years to test the impact of the calibration period length on the two simulations. The results suggest that longer calibration periods improved the GBHM performance during validation periods. In addition, the TRMM simulation is more stable and less sensitive to the calibration period length than is the gauge simulation. Further analysis reveals that the uneven distribution of rain gauges makes the input rainfall data less representative and more heterogeneous, worsening the simulation performance. Our results indicate that remotely sensed rainfall data may be more suitable for driving distributed hydrologic models, especially in basins with poor data quality or limited gauge availability.

Many satellite-based precipitation products have been developed and released during the past decades. This paper presents a primary evaluation and comparison of the two latest products, the Global Precipitation Measurement (GPM) mission Level 3 product Integrated Multi-satellitE Retrievals for GPM (IMERG) and Version 7 Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products. The comparison is based on daily scale data from April 1, 2014, to January 31, 2016, over the entire Mekong River Basin (MRB). Daily observation data from 53 rain gauge stations were obtained to carry out a pixel-point comparison. Various aspects were taken to examine the performance of these two satellite-based precipitation products. The results demonstrate the following: 1) Both IMERG and TMPA overestimate the amount of rain when the rain is light (less than 8 mm at daily scale), and underestimate when the rain is heavy (more than 14 mm at daily scale), and IMERG performs better than TMPA, particularly for reducing the overestimation of light rain; 2) in term of event detection, IMERG more accurately detects rainfall events than TMPA, especially for slight rainfall; 3) IMERG reproduces the probability density function (PDF) of precipitation intensity and captures its intra-annual variability better than TMPA, especially in the Lower Mekong Basin; and 4) both products still have potential for further improvement, particularly applications during the dry seasons and in high-latitude/mountainous regions. This study provides useful information on efficiently deploying IMERG to hydrological studies and for improving IMERG algorithms.

Key Points: Modeling analysis of future hydrologic droughts over U.S. is provided Relative contributions from climate change and water managements are quantified Future hydrologic drought will be alleviated by reservoirs but intensified by local water extraction

Reservoir operations may alter the characteristics of Flood Frequency Curve (FFC) and challenge the basic assumption of stationarity used in flood frequency analysis. This paper presents a combined data‐modeling analysis of reservoir as a nonlinear filter of runoff routing that alters the FFCs. A dimensionless Reservoir Impact Index (RII), defined as the total upstream reservoir storage capacity normalized by the annual streamflow volume, is used to quantify reservoir regulation effects. Analyses are performed for 388 river stations in the contiguous U.S. using the first two moments of the FFC, mean annual maximum flood (MAF) and coefficient of variations (CV), calculated for the pre and post‐dam periods. It is found that MAF generally decreases with increasing RII but stabilizes when RII exceeds a threshold value, and CV increases with RII until a threshold value beyond which CV decreases with RII. Hence depending on the magnitude of RII, reservoir regulation acts as a filter to increase or reduce the nonlinearity of the natural runoff routing process and alters flood characteristics. The nonlinear relationships of MAF and CV with RII can be captured by three reservoir models with different levels of complexity, suggesting that they emerge from the basic flood control function of reservoirs. However, the threshold RII values in the nonlinear relationships depend on the more detailed reservoir operations and objectives that can only be captured by the more complex reservoir models. Our conceptual model may help improve flood‐risk assessment and mitigation in regulated river systems at the regional scale.